Finite and infinite game science: what’s the difference?

Infinite gamers have more fun

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

The distinction between playing with an infinite game mindset and playing with a finite game mindset allows us to unpack how a scientist might bring an infinite mindset to “actual science:” to all those finite games of power and scarce resources scientists play today in the academy. This handbook is all about changing cultures in the academy. Some of these changes are steps forward into new opportunities, others are steps back into the core of how science was done before the recent neoconservative managers arrived on campus. Let’s look at two personas: one, a die-hard finite game player, and the other, a scientist deep into the infinite game.

The finite-game player takes on the role of “The Scientist”

Below are some signs you might be playing the infinite game of doing science as if this were a finite game: playing with a finite-game mindset.

  • The role of “The Scientist” is just a role. You are free to throw this aside at any time. You always have this freedom. At some point you forgot this fact.
  • By forgetting you create a necessity to this role in your life: not for science, however, but for being The Scientist.
  • Entering grad school on a fellowship, you identified avenues of influence you could tap into: you picked a famous scholar to be your committee chair and selected a hot research topic, instead of one from your own interests.
  • You had three papers published by the time you completed your PhD. At least one of these used text “borrowed” from a colleague.
  • Your old committee chair had an inside track to a funding agency that you learned about and cultivated as a post-doc.
  • You jumped into an entry tenure-track position at a different university when your first research proposal was funded, taking your funding with you.
  • At annual meetings of your learned society, you work the publisher booths at to find a sympathetic editor at a high-prestige journal.
  • You push your funded research team of grad students, post-docs, and research staff to make discoveries, or hack the data, to fit the needs of the field’s top impact-factor journal.
  • You ignore those students who don’t perform to your demands and self-funded graduate students, who should realize they don’t belong.
    You shift your lab’s research focus in response to the funder’s new five year strategic plan.
  • When you take on peer review assignments, you are particularly harsh on any work that intersects your own but doesn’t cite you, while you soaked up any useful information about their research methods and findings.
    You leverage your funded research to minimize your teaching load, and you weasel the chair into handing over your undergrad survey class to adjuncts.
  • You use the same textbook for your upper division class that you had as an undergrad.
  • You grade easy to avoid hassles with undergrads.
  • Your graduate methods seminar class promotes your own methods, and critiques others.
  • The platform of your early career wins became a launching pad to grab career advantage over your peers. You search for other early-career winners, and avoid those who aren’t.
  • You constantly eye openings for jobs at higher ranked universities. You make sure you schmooze their department heavy-weights at learned society meetings.
  • You worm your way into volunteer leadership jobs at your learned society, hoping to fast-track recognition.
  • You sit on a couple of major campus-wide strategy committees, instead of curricular or other social committees.
  • Your chancellor gives you opportunities to speak at campus events, where you highlight the research findings you’ve maneuvered to be most glamorous.
  • You mold a social media persona around popular science issues.
  • You craft a high H-index by having your grad students write review articles, which you attach your name to as first author.
  • You haven’t done fully original research or used a new methodology in five years.
  • You carefully hoard your lab’s data, and only publish in journals that do not require open data.
  • You evaluate your colleagues as winners or losers, and steer clear of the latter.
  • You talk about meritocracy in the academy, and believe that’s why you got tenure.
  • You laugh off talk of “work-life balance.” Your work is your life.
  • You fit fully into the role of “The Scientist.” As it colonizes your future, the role of “The Scientist” becomes everything you are and ever wanted. But then you realize you haven’t yet been elected a fellow of your learned society. You worry that you haven’t spent enough time cultivating connections society board members.
  • You’ve never reflected on how your need to harvest your accumulated advantage impacted the quality of your science outputs, nor the career costs of the grad-students you’ve abandoned because they didn’t follow your lead. You never stopped to count the dreams you killed along the way.
  • Because you feel you must play The Scientist continually, you are unable to play science as an infinite game. There is no joy in your work. There is a constant fear that your research results will be proven illegitimate.

A few of the the above activities might be pursued as a finite game by a scientist with an infinite mindset (See: Science is an infinite game). Every scientist is confronted by an academy infested by conflicts of interest and internally validated perverse incentives. As open science works to change the culture, scientists must still forge their careers.

For every finite-game science player who “wins,” dozens more need to “lose.” Scarcity in the system demands this. “Losers” have their careers side-tracked at some point. Their dissertations do not result in high-impact journal articles. Their post-doc opportunities (if available) become dead-ends. The funding agency denies their last-chance research proposal. They migrate away from research institutions to other jobs in and out of academia. The enthusiasm and hope they brought with them as students no longer sustains the energy they need to compete in the finite games of science. They go off and do other work. This is one reason why science loses every time finite-game science player wins.

B) The infinite player does science and plays with the role of “The Scientist.”

Freedom of thought is a fundamental academic freedom. Because science is always shared, this first freedom includes freedom of speech. Freedom is central to infinite game play, where boundaries and horizons, rules and roles, histories and futures are all in flux. Freedom of thought is the infinite-minded scientist’s chief weapon against the silence of nature. Like water, science flows against nature and finds the low spots where new knowledge lurks. Freedom interrupts scientific rigor and intention with the serendipitous discovery.

The infinite player is fully aware that a finite academy game she agrees to play carries a role she admits only to others. She is never “The Scientist” even when she plays one. She does science. She wears the white coat. She shares her findings, her data, her methods, her ideas. She teaches classes that open up infinite play to her students. She talks about awe and about doubt, about method and precision, and how doing science is something more than doing anything else; and it is more, because she plays the infinite game. And when her corner of nature’s mysteries remains silent over months and year, she persists. She knows the game will last when she is gone.

There are finite games in which she has zero interest (to the annoyance of her Chair). She sees no point in crafting a “sexy” P-hacked paper for a high-impact journal. At conferences, she spends most of her time on conversation with students at their posters, or with a few colleagues who occupy the same corner of nature as does she. Chancellors and deans fail to recruit her to campus committees. She risks tenure by focusing on her teaching and her idea of research, on her students and their needs, and on the infinite play that fills her mind day and night. If she must leave this university, she will seek out a college somewhere, with the help of her ex-students, and continue to play.

Still, the innovative thrust of her experiments, the transparent rigor of her methods, the quality of her data (which she freely shares) and the unexpected results these reveal keep getting noticed. Despite her inattention to them, her metrics are stellar. Her generosity is widespread, and well known. She simply lets go of the science goods that are most important to her, knowing that others will remember, and send her new ideas to try out. She is a giver, a genius-maker:

“In Multipliers, former Oracle executive Liz Wiseman distinguishes between geniuses and genius makers. Geniuses tend to be takers: to promote their own interests, they ‘drain intelligence, energy, and capability’ from others. Genius makers tend to be givers: they use their intelligence to amplify the smarts and capabilities’ of other people, Wiseman writes, such that ‘lightbulbs go off over people’s heads, ideas flow, and problems get solved’” (Grant, 2013).

She was denied tenure at her university for ignoring many of the hoops through which she was expected to jump; and immediately hired with tenure at a different university, on the weight and the promise of her research, and the stories about her teaching and mentoring, volunteered from her ex-students. She commonly refuses awards and honors; she calls them distractions.

Even the awe and joy of infinite game play can be easily forgotten; scientists can get lost when they play only finite games with scientific methods and organizational power. These finite games pull their logics from other finite games outside of the academy. These logics tear the academy away from the freedoms that science needs to play its infinite game. The more that the academy is trapped into finite games, the less it gains through open sharing and new opportunities for collaboration and innovation.

The Just Cause(s) of Open Science

“In life, unlike chess, the game continues after checkmate” (Isaac Asimov).

Open science exists to return the everyday life of scientists to infinite game play, to find paths to justice, and to support teaching and research opportunities for scientists everywhere on the planet, in any open institution that will house their work. Open science builds academy commons (plural) where scientists can govern themselves and their resources, maintain and care for their goods and each other, provision their work, and build an abundant future for the infinite game of science across the globe.

Playing the infinite game requires and rewards, demands and builds, encourages and exercises practical wisdom inside science. This type of caring, pragmatic wisdom can carry a scientist, a science team, a laboratory, a school, a university, an agency: any all academy organizations, toward open science, where sharing and caring a not reserved for losers. Where there are no winners, only players. And that is the whole point.

References

Grant, Adam M. Give and Take: A Revolutionary Approach to Success. New York, N.Y: Viking, 2013.

Science is an infinite game you can play

You’ve played this game all your life. When did you decide to stop?

“If there are at least two players, a game exists. And there are two kinds of games: finite games and infinite games.
Finite games are played by known players. They have fixed rules. And there is an agreed-upon objective that, when reached, ends the game. Football, for example, is a finite game. The players all wear uniforms and are easily identifiable. There is a set of rules, and referees are there to enforce those rules. All the players have agreed to play by those rules and they accept penalties when they break the rules. Everyone agrees that whichever team has scored more points by the end of the set time period will be declared the winner, the game will end and everyone will go home. In finite games, there is always a beginning, a middle and an end.
“Infinite games, in contrast, are played by known and unknown players. There are no exact or agreed-upon rules. Though there may be conventions or laws that govern how the players conduct themselves, within those broad boundaries, the players can operate however they want. And if they choose to break with convention, they can. The manner in which each player chooses to play is entirely up to them. And they can change how they play the game at any time, for any reason….
Infinite games have infinite time horizons. And because there is no finish line, no practical end to the game, there is no such thing as ‘winning’ an infinite game” (Sinek, 2019).

In Finite and Infinite Games, James Carse (1987) makes a number of statements about culture and nature, and about human endeavors that include a wide range (perhaps very wide) of everyday human situations.

Here we attempt to insert science as an endeavor into the scheme Carse outlined, with the purpose of grounding the norms of science — however these are described — within science’s infinite play with nature. Of course any scientist — as a biological organism — plays in nature at the same time she plays with nature. To play with nature as a profession is a privilege scientists all share. The finite games of finance or technology might bring in more (perhaps a lot more) money, but the struggle with universal unknowns has its own rewards. The infinite game, as we will see, also ties in complexity theories, emergent systems, explanation and narrative.

Today, science is an endeavor housed in organizations where we find game logics that are mainly finite (When is the next RFP coming out? What’s your H-Score?). This circumstance is in direct conflict with research needs that must — this is the main assertion here — include and support playing the infinite game of science.

Norms point to the infinite game

The notion of the infinite game of science may seem foreign to scientists coached to win finite games to secure their careers. And yet all attempts to capture the normative culture of science hint at an underlying, non-finite game. What we find today is an academy trapped in the contradictions between these two mindsets: the poetry of discovery, the awe of nature, the joy of intellection, and the satisfactions of mentoring have been pushed aside, displaced by the rush for reputation in a now-harshly-competitive system of scarce resources and narrow opportunities.

These contradictions have been noted for decades in articles and books that contrast science’s putative norms with the observable organizational practices of science. Sociologists and critics of science practice point to the realities of doing science in today’s world. “Science claims X, but in practice we find Y.” Ziman (2002) makes this contrast more than seventy times. These observations now share the discourse with a chorus of observations about “bad science:” unreproducible findings, plagiarized and repetitive science articles, ersatz statistics (p-hacking, etc.), systemic biases and conflicts of interest in funding and advancement, public distrust of science findings, and a profiteering publishing industry.

The reality of doing science today seems fundamentally out of step with how good science needs to happen. “Real science” is still distinguished by normative behaviors and values that are regularly called upon to counter deviation into “bad science” (See: Zimring 2019 <https://blogs.scientificamerican.com/observations/were-incentivizing-bad-science/> Retrieved November 8, 2019). But when the incentives are perverse and pervasive, resistance is a challenge that overlays and undermines the challenges of doing infinite-game science. So, what happens when the reality of being a scientist fails to support “real science”?

Doing the right science or doing science right?

“When you rely on incentives, you undermine virtues. Then when you discover that you actually need people who want to do the right thing, those people don’t exist because you’ve crushed anyone’s desire to do the right thing with all these incentives” (Barry Schwartz in Zetter, 2009 <https://www.wired.com/2009/02/ted-barry-schwa/> Retrieved 12/16/19).

Much of the “How” science is played as an infinite game is discussed within the philosophy of science, and the “What” of science fills books in the sociology of science. The infinite game of science explores the “Why” and the also the “just causes” (Sinek, 2019) of science. The “Why” brings us a narrative of science up until this moment, which illuminates its horizon. Science’s just causes point us at the thousands of mysteries, the unknowns that scientists confront today; each mystery offers a bit of new knowledge to be discovered, and the benefits of new understanding. Every unknown also carries a moral load, and the need for judgement in pursuit of justice (more about this below), given that there are many consequences to new knowledge.

Mindsets and practices

Finite and Infinite Games goes into great detail to expose the two mindsets: finite and infinite. These are fundamentally different, and in ways that illuminate many of the issues plaguing the academy today. Here we note only a few key points. First, finite players (players with a finite mindset) play their roles in full seriousness, acquiring their parts as actual and necessary: even when they are always fully free to step out of their parts. They need to forget this freedom in order to play to win. This is really important to keep in mind: finite game players assume their roles as essential parts of who they are, even though they always have the freedom to abandon their role. This mindset lards the role of “scientist” with unnecessary seriousness.

On the other hand, players in an infinite game play with the rules, instead of assuming roles. These rules are constantly changing as players move the boundaries of the game. Infinite games are rule-creating games. The players do not need to accept a set of rules to play. Without stable rules, roles make no sense. Best practices do not apply. Every new experiment opens up its own horizon. In terms of complexity theory, the infinite game demands that you probe, sense and respond (Laloux, 2014, Kurtz and Snowden, 2003) each time you play.

When an infinite player (someone with an infinite mindset) plays a finite game within the infinite game, they do so fully understanding they are simply acting their role, and that they have the freedom to walk away. Yet they still have the capacity to play any finite game to its limit. They can accept the rules in order to play. However, winning or losing has no meaning for them. This may mean they play with greater freedom and abandon, improving their chance of winning.

To remind scientists that their research is an infinite game is to reconnect them to the “one long experiment” (Martin, 1998) that is science. Recent organizational management theories (Sinek, 2019) have put infinite game play and sense-making for complexity (Snowden, 2002; Kurtz and Snowden, 2003; Ito and Howe, 2016) at the center of their recommendations for 21st Century organizational governance. Getting good in the infinite game of science could also build skills that scientists can use to govern their labs, universities, and agencies.

This handbook will help you create new practices that can recenter your university’s values and vision around infinite game play as a strategy for long-term success. Open science is a cultural platform that will connect infinite game players across the globe. You and your organization can join this, or you can continue to play the same bullshit “excellence” games (Moore, et al, 2017; also Neylon[Retrieved Feb. 7, 2020]) you take far too seriously today. And have more fun in your research too.

Science is a war with one long battle

Science, the infinite game, was there with Aristotle and Plato, Bacon and Galileo. With Neuton and Boyle, Einstein and Feynman. And now here, this very moment, with every scientist in and out of the academy. The infinite game of science lies beneath the norms that Merton and others have used to delineate science’s core ethos. The infinite game stands behind every experimental hypothesis and laboratory method. Every time a scientist battles with some mystery of nature, the infinite game continues.

Science works toward horizons and not within boundaries. Scientists see boundaries around them and laugh as they violate these. They go beyond. Any scientist can change the horizon of science and modify the rules of science (for example, by improving a method of observation). Each change in the horizon of science changes the horizon of every scientist.

“The scientist has a lot of experience with ignorance and doubt and uncertainty, and this experience is of very great importance, I think. When a scientist doesn’t know the answer to a problem, he is ignorant. When he has a hunch as to what the result is, he is uncertain. And when he is pretty darn sure of what the result is going to be, he is in some doubt” (Feynman, et al, 2005).

The scientist eats unknowns, and is never full. She sweats doubt. The products of science are not science. These can be destroyed or forgotten and science will continue. Science means challenging the known. Scientists understand how little science knows; that the mysteries they face are mighty. Each scientist picks her own mystery, her own just cause to pursue.

“The earth’s history has been only long experiment, poorly constrained in a reductionist’s eyes. How impoverished the earth would be if had been otherwise” (Martin, 1998).

Science is nature made into poetry

No single scientist speaks for science. No scientist speaks for nature. The speech given by the award winner at the annual convention is not any more scientific than the poster presented by the graduate student. The questions of a student can negate an entire history of discovery.

Unlike the history of society where politics is theatrical and works to close its history (against culture, which keeps this open), the history of science is always dramatic. It is formed by events that must repeat themselves again and again while remaining open to failure, open to a moving horizon that might, and probably will, change and render them false. After that, they join the past history of science and are merely theatrical. One can repeat a failed experiment only as historical theater. The science present moves on in dramatic fashion.

The goods of science inform the knowledge inventory of the world within which science is played. They push science to remake its horizons. They are not unimportant to science but they are not science in the infinite game. Finite-science players want to own these products, in order to garland themselves with prizes. Prized science goods require durability for the value of their prizes to endure. Finite-science players choose to defend their own goods by silencing others and gathering supporters. They seek a past that is closed and known, with their own goods at its front end.

“One must keep in mind that senior faculty probably hold their current positions through their success in the game, which may or may not have been achieved by using the most ethical ways” (Chapman C.A., et al, 2019).

Infinite science players — who know their own research best — interrogate their own findings in search of a larger knowledge horizon. They push the game forward and their egos to the side. They open up to collaborations and seek out conversations with those who disagree with their findings.

Prizes bind science to a known past. This past is carried by science institutions, such as those learned societies that sponsor prizes. These societies also need to endure so that the prizes of finite-science players retain their value. Prize winners and “fellows” carry the weight if ensuring the society persists, warranting the currency of their prizes. However, the continuity of science is not based on an attachment to its past or even it current goods — on the closing of its history — but rather, on a continual openness to surprise, to experiment (Schulz, 2011). Science is based on the nearly universal ephemerality of its findings. Science has always been the child of an open history that will never close.

“[W]hat resounds most deeply in the life of Copernicus is the journey that made knowledge possible and not the knowledge that made the journey successful” (Carse).

Science doesn’t just have a culture. Science also is culture (in Carse’s sense). Like any culture, science is “itself a poiesis, all of its participants are poietai — inventors, makers, artists, storytellers, mythologists. They are not, however, makers of actualities, but makers of possibilities. The creativity of [science] has no outcome, no conclusion” (Carse; paraphrase).

Scientists are ImagiNatives. Poets of the natural world. Makers of possibilities. “It’s been said that science fiction and fantasy are two different things: science fiction, the improbable made possible; fantasy, the impossible made probable” (Rod Serling,“The Fugitive”. The Twilight Zone. Season 3. Episode 25. March 9, 1962. CBS.) Science is nature made into poetry.

“The physicist who sees speaks physics with us, inviting us to see that the things we thought were there are not things at all. By learning new limitations from such a person, we learn not only what to look for with them but also how to see the way we use limitations. A physics so taught becomes poiesis” (Carse).

You cannot do science alone in isolation; do science only in your own mind. This does not mean that you cannot be solitary in your imagination, but only that science happens when you share this with at least one other person. A poet who does not speak has no poetry to speak of. Science happens between and among infinite game players.

The infinite play of science allows no personal power or authority. In a finite game, power always requires opposition and an audience. Neither is available within science. In finite games, winning silences the loser. The personal power that a title conveys; this authority means nothing to science, and usually far too much for scientists. Competition feeds arbitrary power in the academy and defeats science itself, silencing the many to praise the few.

Finite games of prestige in the academy are failings of the academy. Finite games of personal influence and authority contradict the inherent authority of science methods. Scientists are known by their names, not their titles. If your method is transparent and well-founded, your science goods need no amplification beyond their public sharing. The audience that power seeks is not found in science. An infinite game allows no audience. There is no vote that can elevate one science good above another.

Science does not belong to any one society. Science flows across the globe. Change for science has no location, it is always everywhere. Change is always surprising, and so never a surprise. “To be prepared against surprise is to be trained. To be prepared for surprise is to be educated” (Carse). To invite and welcome surprise is to do science. Science creates its activities through fluid consensus, not from any established doctrine, but in response to surprises that happen whenever science moves its horizon.

“[As it is in nature, so] also in [science]. Infinite players understand that the vigor of [science] has to do with the variety of its sources, the differences within itself. The unique and the surprising are not suppressed in some persons for the strength of others. The genius in you stimulates the genius in me” (paraphrase: Carse).

Every science effort begins and ends in surprise. Because the next instant of knowing is always open, the moment of discovery is always surprising. This is a source of joy for the scientist. If the object of research were already known or fully predictable, the research is unnecessary. Reproducibility means that the same effort must result in the same surprise. The first effort exposes the scientist to this surprise. The second time gifts this surprise to science.

In the infinite game, science invests more authority on the rigor of its methods than it does in the sagacity of its practitioners. The results of well-constructed experiments are all discoveries, even when the results are null. Each experiment extends the horizon of the game.

Who wants yesterday’s findings?

The final page of science will never be written. A new finding is lightly penciled in after the previous paragraph on the current page. Every infinite player brings their own eraser to this book. Chapters long settled and well considered can be erased in a single day. That is a joy for science. New pages open up then. New horizons emerge. The game accelerates. More players find ways to add new paragraphs to this ledger. Ever since Bacon, yesterday’s findings hold less knowledge than tomorrow’s.

Scientific surprise is mainly retold as serendipity. Serendipity, the unexpected confluence of curiosity and sagacity, is just another way of announcing that scientists are playing the infinite game.

“The paradox in our relation to nature is that the more deeply [science] respects the indifference of nature, the more creatively it will call upon its own spontaneity in response. The more clearly we remind ourselves that we can have no unnatural influence on nature, the more our [science] will embody a freedom to embrace surprise and unpredictability” (paraphrase) Carse.

What is real science again?

“The notion that academic scientists have to be humble and disinterested… seems to contradict all our impressions of the research world. Scientists are usually passionate advocates of their own ideas, and often extremely vain” (Ziman, 2002).

We have all read studies and stories of “actual” science that highlight how scientists live and work in the “real world.” Today, scientists labor within the pragmatic circumstances of the increasingly neo-liberal academy — surrounded by an increasingly neo-liberal global economy: a world of intense competition for fame and funding, a space of accumulated advantages for a few, and increasing precarity for the rest. Yet the moments of science, the methodic but often serendipitous event of discovery, yank the scientist back into a different “real” — the real task of uncovering new knowledge about the actual “real world” — the natural universe. To do science is to play an infinite game with nature.

References

Carse, James P. Finite and Infinite Games. Ballantine Books, 1987.

Chapman, Colin A., Júlio César Bicca-Marques, Sébastien Calvignac-Spencer, Pengfei Fan, Peter J. Fashing, Jan Gogarten, Songtao Guo, et al. “Games Academics Play and Their Consequences: How Authorship, h -Index and Journal Impact Factors Are Shaping the Future of Academia.” Proceedings of the Royal Society B: Biological Sciences 286, no. 1916 (December 4, 2019): 20192047. https://doi.org/10.1098/rspb.2019.2047.

Feynman, R.P., J. Robbins, H. Sturman, and A. Löhnberg,. The Pleasure of Finding Things Out. Nieuw Amsterdam, 2005.

Ito, Joi, and Jeff Howe. Whiplash: How to Survive Our Faster Future. Grand Central Publishing, 2016.

Kurtz, Cynthia F, and David J Snowden. “The New Dynamics of Strategy: Sense-Making in a Complex and Complicated World.” IBM Systems Journal 42, no. 3 (2003): 462–483.

Laloux, Frederic. Reinventing Organizations: A Guide to Creating Organizations Inspired by the next Stage in Human Consciousness. Nelson Parker, 2014.

Martin, Ronald E. One Long Experiment: Scale and Process in Earth History. Columbia University Press, 1998.

Moore, S., C. Neylon, M.P. Eve, D.P. O’Donnell, and D Pattinson. “‘Excellence R Us’: University Research and the Fetishisation of Excellence.” Palgrave Communications 3 (2017): 16105.

Schulz, Kathryn. Being wrong: Adventures in the margin of error. Granta Books, 2011.

Sinek, Simon. The Infinite Game. New York: Portfolio/Penguin, 2019.

Snowden, David. “Complex Acts of Knowing: Paradox and Descriptive Self-Awareness.” Journal of Knowledge Management 6, no. 2 (2002): 100–111.

Ziman, John. Real Science: What It Is and What It Means. Cambridge University Press, 2002.

The practical wisdom in doing science

Science calls for wisdom in the face of wicked problems

“If you compromise your integrity and principles on minor issues, it gets easier to make bad choices on the big issues” (Dunwoody and Collins, 2015).

“Do the right thing. It will gratify some people and astonish the rest” (Mark Twain, attrib.).

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

The current research on “wisdom” defines this in approximately as many ways as there are wisdom researchers (Sternberg, 1990). Fortunately, we do not need to lock down a definition of wisdom here to see its lack in the academy, and the benefits of promoting this as one more measure of what it means to do science and to be a scientist today. Most of the research on practical wisdom confirms what you already think about this. People who are wise in this way know how and when to do the right thing in a broad range of circumstances.

It’s not all that simple, of course. Doing the right thing is not merely “being right,” and most certainly is not thinking you’re right, and convincing everyone else how wrong they are. Practical wisdom operates in the infinite game of open, complex intellectual and normative choices, and conflicting and ambiguous circumstances.

The “right thing” is rarely an easy binary operation and it often requires insights and emotional commitments beyond a simple logic. It may also be an action that goes against the immediate best interests of others or the wise person. “[M]any who have written on wisdom have identified it with the ability to develop and defend good judgments about the difficult, wicked-decision problems characteristic of adult life” (Kitchener and Brenner, 1990).

Mostly, practical wisdom examples involve interpersonal or socioeconomic decisions. These examples map how individuals wend their life together with others and the world. This part of practical wisdom encompasses careers in and out of the academy. You can explore the more general forms of practical wisdom literature elsewhere (Practical wisdom: The right way to do the right thing by Schwartz and Sharpe (2010) is a good place to start, also Barry Schwartz’s TED talk [Retrieved January 7, 2020] or his WIRED interview). So, what forms of practical wisdom are peculiar to doing science?

In finite games, where arbitrary scarcity and external incentives warp the moral fabric of interaction, practical wisdom often gives way to self-promotion strategies. Somebody has to lose. Actually, most people need to lose in order for winning to matter. In the infinite game of science, nobody wins or loses, and the main strategies include sharing knowledge and adding new players to the mix. Science loads additional wicked-problem solving on top of the adult life problems of work and home. Wise researchers confront the silent unknowns of a complex and emergent natural world.

The ability to start from observations and data about the world and transform these into information, knowledge, and understanding is an inherently moral activity; each bit of new knowledge — big or small — changes existing rules for every scientist and expands the envelope of possible human action. Medical science practice is often cited as a key discipline in need of practical wisdom because of its everyday moral decision making (See: Branch and Mitchell, 2011; Kaldjian, 2010; and Jeste, et al, 2019). All science domains are similarly implicated when they enter into infinite game play.

How does practical wisdom improve research?

Judith Glück (2017) makes a case for practical wisdom in the academy. The first behavior is a desire for a deep understanding of complex emergent systems, instead of a personal claim about some potentially universal truth. This is precisely the difference between playing the infinite game of science, and playing a finite zero-sum game. “Over time, wise researchers’ desire to thoroughly understand should lead them to develop an extraordinary amount of knowledge: a broad and deep integrative understanding of a subject matter that includes a keen awareness of what they do not (and may never) know” (ibid). In short, they become intellectually humble.

“[Humble intelligence is] a method of thinking. It’s about entertaining the possibility that you may be wrong and being open to learning from the experience of others. Intellectual humility is about being actively curious about your blind spots. One illustration is in the ideal of the scientific method, where a scientist actively works against her own hypothesis, attempting to rule out any other alternative explanations for a phenomenon before settling on a conclusion. It’s about asking: What am I missing here?” (Resnick, 2019 <https://www.vox.com/science-and-health/2019/1/4/17989224/intellectual-humility-explained-psychology-replication> Retrieved June 7, 2019).

Practical wisdom in the academy is built upon the humble intelligence of each scientist. As science becomes more collaborative, networking and teamwork bring new demands on the practical wisdom of every member. As we first learned in Ten activities to work on, Tangney (2000) proposes that intellectual humility requires five abilities:

A. the ability to acknowledge mistakes and shortcomings;

B. openness to perspective and change;

C. an accurate view of the self’s strengths;

D. ability to acknowledge and experience life outside the direct consciousness of the self; and,

E. the ability to appreciate the worth of all things.

Each of these abilities adds value to any academy collaboration effort. Open science captures this value by promoting equality of access and networked collaborative opportunities.

The greater good

The second wise-researcher behavior is a high level of concern and care for the “greater good”: for the welfare of the entire “Republic of Science” (Polanyi, 1962), of the next generation of scientists, and of the planet:

“Wise researchers will be concerned with the well-being of others, ranging from their students to the world at large. Inside the university, wise researchers will care about the quality of their teaching. … Wise researchers are also caring, generative mentors who seriously work on supporting the career development of their mentees and genuinely enjoy their success”(Glück, 2017).

The care that wise researchers bring to their science reflects their ability to set their own ego aside. Many of the distinctions between practical wisdom and general intellectual accomplishment pivot on this attention to the greater good: to the underlying reasons, the overarching effects, the larger, messier, more complex consequences of new knowledge.

Open-access science publishing can support the wise researcher looking to share their whole research in exchange for access to the research of their colleagues. New evaluation tools that reward ethical behaviors and a much greater variety of goals and social activities would support the efforts of those with a recognized concern for the welfare of their mentees and for the research success of others. Assessment tools that capture a diverse and incremental landscape of work outputs and socially-supportive activities are already being developed in the workplace outside the academy (See: Buckingham and Goodall, 2019).

Practical wisdom is also an antidote to assholes in the academy. The Handbook deals with assholes elsewhere (See: The Need for a Zero-Asshole Zone). Most wisdom research concludes that you cannot have practical wisdom and also be an asshole or evil (See: Stanford Encyclopedia of Philosophy: Wisdom, 2002). In part, this is because you cannot have practical wisdom without actually using your practical wisdom. As noted elsewhere in the Handbook, much of the assholic behavior in the academy is learned and rewarded today; and so, most academy assholes are not irredeemable. They learn and use bad behavior to win the finite games currently infecting the academy. They can unlearn these behaviors and gain some practical wisdom over time. Some, however, are true assholes, and will not change (See: The bright and the dark). These will resent and resist change when your new open-science cultural practices no longer support their bad behavior.

How do we build the practices of humble intelligence and a care for the greater good into the academy? Such is the project of this Handbook, a guide to changing culture in your precinct of the academy, wherever this is. Open science builds fierce equality into the academy as a normative behavior that expresses intellectual humility and inclusiveness. In the infinite game of science new knowledge can be found by anyone in the Republic of Science. Open science uses demand sharing to support the greater good. As a sharing economy, the academy’s goods gain value across time and space. Science is the hardest thing you can do (after child rearing). Take a minute to remember how challenging science really is. Doing science really well will change you as a person, even as you change the world of science through your efforts.

The starting point for culture change is, as always, changing yourself. Apart from a few dark-core types, everyone can gain practical wisdom, even tenured faculty. Start today. Be wiser tomorrow.

Wisdom learning starts in childhood, but need not end there

Some researchers point out that a fair amount of practical wisdom is learned throughout childhood, mostly during play (See: Feist, 2006; Brown, 2009; Carlson and White, 2013; and Sharma and Dewangan, 2017). If you are no longer a child as you read this, do not worry, you can still catch up. To begin with, play is always available even for adults (See: <https://eachother.org.uk/the-right-to-play-adults/> Retrieved January 5, 2020).

While the “skills” of practical wisdom — and wisdom in general — cannot be gained through the same type of specific practice as, say, a violin, or a golf swing, practical wisdom is similarly experience-based (Cantrell and Sharpe, 2016). Like most cultural practices, you can get better at practical wisdom through practice, it is not an inherent trait. You are not born with all the wisdom you can have nor what you might really need to use for your career in the academy.

Glück and Bluck (2103) demonstrate a model for acquiring personal wisdom based on a “strong sense of mastery, high levels of openness, a reflective attitude, and emotion regulation skills combined with empathy.” This “MORE” model can be used in “leadership” curriculum development at the undergraduate level (Sharma and Dewangan, 2017). The Phronesis Project [Retrieved January 8, 2020] at the University of Virginia School of Medicine is pioneering practical wisdom training in its curriculum. Just remember that you can also have fun while becoming wiser, you enjoy learning, after all, so learning practical wisdom is an opportunity to master another life skill:

“Another value of science is the fun called intellectual enjoyment which some people get from reading and learning and thinking about it, and which others get from working in it” (Feynman, et al, 2005).

Schwartz and Sharpe (2010) find that institutions that manage behaviors through rigid rules or laws crowd out opportunities for members to gain wisdom from self-governed interactions. “[I]t’s important to resist those rules and incentives that eviscerate discretion and threaten wisdom. That’s why we need to reform those institutions that are driving wisdom out.” It could be that your institutions would benefit from more democracy and healthy arguments, and fewer rules. The Handbook sections on Learning Organizations are a good place to start.

References

Branch Jr, William T, and Gary A Mitchell. “Wisdom in Medicine.” Pharos Alpha Omega Alpha Honor Med Soc 74 (2011): 12–17.

Brown, Stuart L. Play: How It Shapes the Brain, Opens the Imagination, and Invigorates the Soul. Penguin, 2009.

Buckingham, Marcus, and Ashley Goodall. Nine Lies about Work: A Freethinking Leader’s Guide to the Real World. Boston, Massachusetts: Harvard Business Review Press, 2019.

Cantrell, Deborah J, and Kenneth Sharpe. “Practicing Practical Wisdom.” Mercer L. Rev. 67 (2015): 331.

Carlson, Stephanie M, and Rachel E White. “Executive Function, Pretend Play, and Imagination.” The Oxford Handbook of the Development of Imagination, 2013, 161–174.

Dunwoody, Ann, and Tomago Collins. A Higher Standard: Leadership Strategies from America’s First Female Four-Star General. First Da Dapo Press edition. Boston, MA: Da Capo Lifelong, a member of the Perseus Books Group, 2015.

Feist, Gregory J. The Psychology of Science and the Origins of the Scientific Mind. New Haven: Yale University Press, 2006.

Feynman, R.P., J. Robbins, H. Sturman, and A. Löhnberg,. The Pleasure of Finding Things Out. Nieuw Amsterdam, 2005.

Glück, Judith. “… and the Wisdom to Know the Difference: Scholarly Success From a Wisdom Perspective.” Perspectives on Psychological Science 12, no. 6 (November 2017): 1148–52. https://doi.org/10.1177/1745691617727528.

Glück, Judith, and Susan Bluck. “The MORE Life Experience Model: A Theory of the Development of Personal Wisdom.” In The Scientific Study of Personal Wisdom, 75–97. Springer, 2013.

Jeste, Dilip V., Ellen E. Lee, Charles Cassidy, Rachel Caspari, Pascal Gagneux, Danielle Glorioso, Bruce L. Miller, et al. “The New Science of Practical Wisdom.” Perspectives in Biology and Medicine 62, no. 2 (2019): 216–36. https://doi.org/10.1353/pbm.2019.0011.

Kaldjian, L. C. “Teaching Practical Wisdom in Medicine through Clinical Judgement, Goals of Care, and Ethical Reasoning.” Journal of Medical Ethics 36, no. 9 (September 1, 2010): 558–62. https://doi.org/10.1136/jme.2009.035295.

Kitchener, Karen Strohm, and Helene G Brenner. “Wisdom and Reflective Judgment: Knowing in the Face of Uncertainty.” Wisdom: Its Nature, Origins, and Development, 1990, 212.

Polanyi, M. “The Republic of Science: Its Political and Economic Theory.” Minerva 1 (1962): 54–73.

Schwartz, Barry, and Kenneth Sharpe. Practical Wisdom: The Right Way to Do the Right Thing. Penguin, 2010.

Sharma, Ankita, and Roshan Lal Dewangan. “Can Wisdom Be Fostered: Time to Test the Model of Wisdom.” Edited by Feng Kong. Cogent Psychology 4, no. 1 (September 21, 2017). https://doi.org/10.1080/23311908.2017.1381456.

Sternberg, Robert J. Wisdom: Its Nature, Origins, and Development. Cambridge University Press, 1990.

Tangney, June Price. “Humility: Theoretical Perspectives, Empirical Findings and Directions for Future Research.” Journal of Social and Clinical Psychology 19, no. 1 (2000): 70–82.

Kindness, Culture, and Caring: The Open Science Way

Time for dignity and fairness: and the value of caring. Artwork by Kelvy Bird: https://www.kelvybird.com/

“So it’s kind of like if your house catches on fire. The bad news is there is no fire brigade. The good news is random people apparate from nowhere, put out the fire and leave without expecting payment or praise. …I was trying to think of the right model to describe this form of random acts of kindness by geeky strangers. …You know, it’s just like the hail goes out and people are ready to help. And it turns out this model is everywhere, once you start looking for it.” Jonathan Zittrain, Ted Talk 2009 <https://www.ted.com/talks/jonathan_zittrain_the_web_is_a_random_act_of_kindness?>

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon. All comments are welcome! Revised from a talk at the ESIP 2019 Summer Meeting. With travel support from the Alfred P. Sloan Foundation. Arigatou!

There are lots of ways that the rational, logical, hyper-competitive, winner-take-all, zero-sum, prisoner’s dilemma, nice-guys-finish-last, single-bottom-line, annual-productivity ratchet — or add your adjective here — mindset is just wrong for sustaining the academy and bad for science. For decades now, the same neo-liberal economic schemes that have been used to reshape how governments budget their funds have also made dramatic and disturbing inroads into university budgets and governance. Open science can show how that trend is a race to the bottom for universities. What do you say, we turn around and go another way?

“I have learnt silence from the talkative, tolerance from the intolerant and kindness from the unkind.” Khalil Ghibran, Sand and Foam.

A century without kindness: the impact of external logics

The banishment of kindness as a necessary part of being an academic, — just one more feature of adopting the neoliberal marketplace logic, and another effect of hyper-masculinity in the workplace — allows academics to defer judgements about kindness:

“We want to argue, however, that although kindness is a commonplace in pedagogical encounters, easily recognisable by its presence or absence, attending to it can be subversive of neo-liberal assumptions that place value on utility and cost above other human values” (Clegg and Rowland, 2010).

The word for kindness in Latin is humanitas: kindness makes us human. “[T]he Roman Emperor Marcus Aurelius, a leading Stoic philosopher, speaks of kindness as ‘mankind’s [sic] greatest delight’ (Phillips and Taylor 2009, 18). In Aristotle’s teachings, kindness is a component of phronesis: an entire type of “practical wisdom” that we’ve slowly devalued over the past 300 years (Juarrero, 1999) [ and you can blame Hume and Kant and all the usual suspects for this]. Phronesis combines virtue with a notion of adult comprehension: a way of knowing the right thing to do in all circumstances. It has little to do with intellection, and everything to do with broad experience and learning.

The road to a doctorate is long and difficult, and so adding another layer of learning to the process might seem short-sighted. And yet avoiding learning phronesis in your daily life is probably not any easier than practicing this, since the absence of phronesis leads to serial mistakes in moral and practical judgement, any one of which can be “career defining” in a negative sense. “Practical wisdom” is integral to “doing the right thing” while you learn to “do the thing right.” Doing the right thing often includes knowing how to exercise kindness with others.

A child can show kindness, and we welcome this. An adult (one who has learned some phronesis) can act kinder than a child, because this adult is experienced in a broader range of social circumstances and personal relationships. An adult can be — to use the Yiddish — a mensch. And a mensch can be kinder than a non-mensch or a proto-mensch. Lesson: be an open science mensch.

Kindness starts with intention

Real kindness begins with a clear intention. This adds an important aspect of self-judgement to its base. Without this aspect you cannot actually be kind, even if others might interpret what you are doing as being kind. How do you actually judge your intentions, particularly in relationships with other people and things? Something to contemplate. Also note: Clegg and Rowland (2010) remind us that kindness is not equated with leniency or “being nice.” Real kindness uses courage to articulate accurate observations and open learning moments that can be difficult and painful for both parties.

Kindness is something you learn and do

Kindness is a normative human practice in a wide range of social frames: parenting, friendship, governance, teaching, caregiving, civil interactions. Zittrain (above) reminds us that the internet was built on kindness and generosity. In nearly every human social endeavor, kindness matters. Even in highly-competitive sporting events, “sportsmanship” is highly valued, and is actually an internal normative form of kindness. Why should kindness, and critical interrogations about its role, be absent from research and management in the academy?

Like rationality, kindness is a form of practice, not an emotion. You can no more “feel” kind than you can “feel” rational. Unlike rationality, kindness necessarily involves others, their perspectives and needs. Kindness can and will also be judged by others for its qualities. Is it genuine? Is it motivated by a need to be perceived as kind? Is it effective in performing its intention? What is its intention? In the academy where intellectual judgements run wide and deep, kindness opens up another opportunity to be judged. But so does being unkind. Or it should. For decades, the lack of kindness in our research institutions and workplaces has gone unremarked. It is time to remark these.

Culture provides meaning to intentions

Again, kindness begins with intention. The same activity with different intentions can be a kind, caring conversation, or it can be a cruel interrogation. Intentions are themselves colored by culture. Culture provides a layer of shared meaning/learning that helps the individual (both the intend-er and the intend-ee) discover and interpret shared meaning as intended. You and the other person can answer the question: what did you mean?

The social world always contains this layer of culture. There is no society without it. Individuals hold this layer as a shared/learned resource. The cultural values (See: Values, freedoms and principles) you bring to your open science organization can assemble the meanings that add clear intentions to shared kindness. Just as some institutional cultures today — and inside the academy — support bullying and demeaning actions (NAS et al, 2018).

One feature of kindness is that it enables both halves of the double meaning of the term “care.” To really care about someone or something, you need to tap into genuine kindness. To care for someone or something can merely be a job. But this job is also reduced without the impulse of kindness. That is why it is time to…

Put care back in your career.

“[B]y infusing bureaucratic maintenance work with an ethic of care, we can challenge contemporary workplace attitudes surrounding “productivity” and “efficiency,” moving toward the recognition of maintenance itself as a valued contribution. We can also broaden access to systems of information, thereby supporting its generative value…” (Maintainers et al, 2019).

The Maintainers <http://themaintainers.org/> extend an ethic of care to each other and to their work: they keep everything running, instead of inventing new stuff. This ethic is born in kindness, and requires a level of humility not casually found in the academy, where intellectual heroics overshadow moral choices.

Nel Noddings, who is a “care theorist,” someone who makes “the caring relation basic in moral theory” (2003), looks to recenter care as a normative behavior in education and the academy. She also separates the care that is expected in work (for example, doing something really well, or managing the needs of a student/patient) as a conformity to a workplace ethic, from caring: human acts “done out of love and natural inclination” (Noddings, 1988). What really works — in teaching and learning, and in team dynamics for collaborative research — is not completing the task of due-diligence, but rather building a framework of mutual caring nurtured from authentic kindness.

Bringing care into this discussion has now moved us away from communities, cohorts, and institutions. Care directs us back to intentions that are articulated in culture, but which also speak to being human in a mutually responsible human environment: a phrase not usually descriptive of the academy. “[W]e are led to redefine responsibility as response-ability, the ability to respond positively to others and not just to fulfill assigned duties” (ibid).

Open science is also science done through care and kindness: science that much more resembles the model of peer production within a commons, than it does a winner-take-all corporate struggle. “[W]ithout receiving conventional, tangible payments or favors in return, peers exercise kindness, benevolence, charity and generosity” (Benkler and Nissenbaum, 2006). Open science demands new levels of response-abilities: based on new and expanded academic freedoms (See: Values, freedoms and principles) and internet-enabled collaborations.

Coda: There are a lot more articles and books (such as Phillips and Taylor (2009): On Kindness) about the history of kindness and care that point out how these virtues were heralded as the basis of human happiness for centuries, and only recently (last 3–400 years) have these been eclipsed by more individualistic moral models (thanks to Hobbes, Kant, etc. — the usual suspects). So… practicing open science may also be good for your happiness. Doing open science can improve your happiness, and the happiness of those around you. How about that?

References

Benkler, Yochai, and Helen Nissenbaum. “Commons-Based Peer Production and Virtue.” Journal of Political Philosophy 14, no. 4 (2006): 394–419.
Clegg, S., and S. Rowland. “Kindness in pedagogical practice and academic life.” British Journal of Sociology of Education 31, no. 6 (2010): 719–735.
Juarrero, Alicia. Dynamics in Action. Cambridge, Ma: MIT Press, 1999.
National Academies of Sciences, Engineering, and Medicine, NAS Committee on the Impacts of Sexual Harassment in Academia, Committee on Women in Science, Engineering, and Medicine, and Policy and Global Affairs. Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine. Edited by Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya. Washington, D.C.: National Academies Press, 2018. https://doi.org/10.17226/24994.
Noddings, Nel. “An Ethic of Caring and Its Implications for Instructional Arrangements.” American Journal of Education 96, no. 2 (1988): 215–230.
— — — . Happiness and Education. Cambridge University Press, 2003.
Phillips, Adam, and Barbara Taylor. On Kindness. 1st American ed. New York: Farrar, Straus and Giroux, 2009.

10 things every open-science culture-change agent needs to know about.

Here are the 10 things you need to know about to be an open-science culture-change agent. Pick the ones you want to challenge yourself to master.

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

Sharing starts everything Photo: Steve Jurvetson on Flickr.

1. Open science culture starts with the logic of demand sharing:

This is the same logic used to teach science in classrooms: knowledge gains value when it is shared. The more it is shared the more it is worth; the faster it is shared the greater its impact; the wider it is shared the better the chance that someone else will improve upon it, and share this improvement back with you.

2. Intellectual humility is integral to open science:

“The humility of scientific genius is not simply culturally appropriate but results from the realization that scientific advance involves the collaboration of past and present generations” (Merton, 1973).

Here are some aspects of humility and reasons why this is a great fit with open science, and a powerful agent against bullshit prestige (Moore, et al, 2017) and narcissism (Lemaitre, 2015) in the academy. Tangney (2000) constructed a working definition of humility, one that is not simply philosophical, but also informed by social and interpersonal circumstances. This definition rejects humility as a psychological weakness, instead, humility demonstrates a range of abilities highly valuable in the conduct of science. According to Tangney, humility has five elements:

A. the ability to acknowledge mistakes and shortcomings;
B. openness to perspective and change;
C. an accurate view of the self’s strengths;
D. ability to acknowledge and experience life outside the direct consciousness of the self; and,
E. the ability to appreciate the worth of all things.

As an open scientist (or just someone who wants to do science really well), you might consider how to develop all of these capabilities. You acknowledge your mistakes in order to learn new facts; you broaden your perspectives on your topic to achieve a wider level of understanding; you evaluate your own skills to discover where you must improve your methods; you journey into the unknowns in your field to stretch the envelope of our knowledge; and you reserve judgement on the work of others long enough to fully grasp their meanings. You also give others more attention and respect. This does not mean you respect yourself any less. You just learn to step around your ego to see others and their work as more valuable. Recent research has found that intellectually humble individuals may acquire new knowledge better than others (Krumrei-Mancuso, et al, 2019). Also note: “only humility can navigate complexity” (Fred Kofman <http://www.youtube.com/watch?v=80vYx7ufzZI&feature=relmfu> Retrieved September 14, 2019).

Humility helps you learn. Humility enables your research. You are a scientist: you have the freedom to be humble about it. It’s not modesty. Nobody is asking you to be modest. Think of it more as “hum-ability”.

In Aikido, humility becomes hum-ability. Open science is the same.

3. Intentional kindness is the platform for open science culture:

“The power of happiness, kindness and humility in the competitive academic environment is underrated, but I firmly believe that they are a force for change for the good of scientific practice. In my opinion, widespread application of these principles could vastly improve the quality of life of scientists and university professors worldwide” (Maestre 2018).

Kindness in open science (end elsewhere) begins with intention. Intentions are themselves colored by culture. Culture provides a layer of shared meaning/learning that helps us discover and interpret and map shared meaning as intended. The same conversation with different intentions can be a kind, caring dialogue, or it can be a cruel interrogation. The cultural values (See: Values, freedoms and principles) you bring to your open science organization can assemble the meanings that add clear intentions to acts of kindness, and to the generosity that all science requires. Just as some institutional cultures today — and inside the academy — support bullying and demeaning actions (NAS et al, 2018). Note: kindness does not mean weakness.

Shared kindness is a platform that lifts open science up to new potentials for sharing knowledge. In the academy, kindness is a radical form of courage. Everyone here is smart. If you want to truly distinguish yourself: be kind.

Kindness flows from a concern for the whole science community and the planet, not just your own lab or students. The best teachers are already kind in their classrooms. Bring that kindness to your research too. Don’t be that one asshole who makes others stop sharing. Kindness is not optional.

4. Open science means really open:

Open science may have started by opening up paywalled publication workflows, but it only succeeds when open extends back through the whole research process. Open is a manner of doing research that seeks to reveal as much of itself as it can or might, to promote shared knowing and reproducibility. Open is a transparently governed and democratic workplace in your organization. Open is open across the planet.

5. Open-science culture change starts with you:

Now is the time for you to lead your own open-science cultural change project. When you look around, you might be dismayed by the (dead) weight of organizational culture in your workplace. You can start small, and you can recruit others. The goal is to get back to the way science is meant to be pursued: to play the infinite game against intractable unknowns, to squeeze new knowledge from observations and information.

Remember first that leadership means humble conversations (Deep Dive: Humble Conversations ), fear-free interactions (Deep Dive: The Fear-Free Organization), democratic participation (Deep Dive: Democracy). You provide the compass (an informed open-science perspective), not a map. You and your colleagues are on a new learning curve toward a workplace where the only fear you find is the joyful thrill of playing with nature and data to unlock new insights. Open science needs you to find this kind of leadership inside and bring it to the academy.

6. Open science culture is learned:

You learn culture just like you do science, only you started early on, and without knowing this. That’s what this handbook is for. Disney and the Boy Scouts have been conscious, intentional, culture-learning organizations for decades. So too has the US Navy (for example), and your elementary school. This Handbook and hundreds of web resources are available help you discover more about open science and how to be an open scientist.

Culture is not just some subliminal vibe that you soaked up somehow (although you did a lot of culture learning really early on, and it seems like it was just soaked up). You are an adult. You are now responsible for your cultural behaviors. You can bring your focused intention and behavioral skilling to the goal of becoming more open each day. You succeed as an open scientist (and, in some fashion, as a person) by being more open today than you were yesterday.

7. Open science culture is an on-going conversation:

Make it a point to talk and question others about open science culture. The more people who talk the talk, walk the walk, and share what they value most, the better science will become. As John Wilbanks once said: “the opposite of ‘open’ is not shut. The opposite to open is broken.” Share your open science practices and stories. Keep talking with one another as you build common agreements.

8. Open science culture must be transmitted:

Teach your students to be open scientists. Talk to you children about how science really works in the open. Talk to their science teachers about the benefits of open science. Be an open-science mom or dad. Don’t have kids? Make sure the freshmen in your class know the difference between old-science and open science. The next generation of open scientists will need to assemble their own cultures. You can give them a head start.

9. Open science means open to all:

Not just to the “Republic of Science,” to the long-tail and beyond. Publication works when anyone on the planet can find your knowledge and share theirs with you. Do not worry; technology will help provide filters to keep you from drowning in information you do not need. Technology is one side of being open. Culture is the other side. The entire planet gets into the act at some point.

Future open scientists. Photo: Steve Jurvetson on Flickr.

10. Open science culture will become your culture too:

You get to grow your own personal virtues aligned with the shared virtues you use in your work. You get to add passion (and nuance too) to how you realize your own cultural flavors within your various social/workplace groups. Open science wants as much of you as you care to bring to it. You can take and carry away as much open science culture as works for you. You can own your unique style of open science. Grow it. Show it off. Add new thoughts to the mix. Make a ruckus with it.

References

Krumrei-Mancuso, Elizabeth J., Megan C. Haggard, Jordan P. LaBouff, and Wade C. Rowatt. “Links between Intellectual Humility and Acquiring Knowledge.” The Journal of Positive Psychology, February 14, 2019, 1–16. https://doi.org/10.1080/17439760.2019.1579359.
Lemaitre, Bruno. An Essay on Science and Narcissism: How Do High-Ego Personalities Drive Research in Life Sciences? Bruno Lemaitre, 2015.
Maestre, Fernando T. “Seven Steps towards Health and Happiness in the Lab.” Nature, November 23, 2018, d41586–018–07514–17. https://doi.org/10.1038/d41586-018-07514-7.
Merton, R.K. The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago press, 1973.
Moore, S., C. Neylon, M.P. Eve, D.P. O’Donnell, and D Pattinson. “‘Excellence R Us’: University Research and the Fetishisation of Excellence.” Palgrave Communications 3 (2017): 16105.
National Academies of Sciences, Engineering, and Medicine, NAS Committee on the Impacts of Sexual Harassment in Academia, Committee on Women in Science, Engineering, and Medicine, and Policy and Global Affairs. Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine. Edited by Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya. Washington, D.C.: National Academies Press, 2018. https://doi.org/10.17226/24994.
Tangney, June Price. “Humility: Theoretical Perspectives, Empirical Findings and Directions for Future Research.” Journal of Social and Clinical Psychology 19, no. 1 (2000): 70–82.

Is My Learned Society Obsolete?

“[M]any people, especially those in positions of influence, strive to ‘do things better,’ which in practice amounts to ‘do obsolete things better’”(Ted Dintersmith, What School Should Be, 2018).

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon. AND you can help by adding your comments now.

But we do such good work…

Is my learned society obsolete? This is just one question any open scientist hired on staff, or volunteering as a leader at a learned society needs to ask herself, and then others. In the last century and before that, learned societies flourished as homes for disciplinary, sub-disciplinary (and sub-sub-disciplinary) journals and annual conversations. They also provide a lobbying voice for their segment of the science endeavor, which itself may be obsolescent. They offer a place for new scholars to meet established scholars, and they recognize and reward exemplary work.

Michelle Brook <https://quantumplations.org/> did a count of learned societies in the UK, and found more than eight-hundred of these. Across the globe there are thousands. One can imagine that each one of these does some good within its purview. The other side this good, however, is the opportunity cost of their work (members might get more value elsewhere), and their relative ability to contribute to and benefit from open science. In the face of opportunities and change, these societies will all need to find nimble footing going forward:

“[L]earned societies are part of the UK’s knowledge economy and they can expect to see the pace of change and external competition increasing, so having a forward-thinking, adaptable and change-welcoming culture is important to their future survival (Gardner, 2013).

If your learned society is, in part, or on the main, obsolete, then making these obsolete bits better is the wrong way to spend your energies. Instead, you need to start the process of replacing obsolete practices, behaviors, and attitudes. This will be hard, but there are good resources here in the Handbook to help you out. So, what does obsolescence look like in the academy?

Note: there are lots of other (organizational, attitudinal, etc.) ways that your society may be obsolete. Here we will just list some of the ones that impact its role in open science.

Seven obsolete features still found in learned societies:

1. Journals/monographs that are based on the production of a paper product, and distributed through a subscription model

The form of a paper journal creates an arbitrary scarcity to the publication. It’s like you are publishing and privatizing the work of your members at the same time. University libraries are cajoled into subscriptions that must then be renewed for continuity. Members are tasked to provide peer review. Most likely you already do a digital version, anyhow.

Step away from the cellulose and subscriptions and use the Internet. It’s been around for decades. Your scientist readers around the globe will approve. Do you pay for what you do (outside of publishing/privatizing the journal) with moneys from subscriptions? First, you’ve been charging too much. Second, if what you do (scholarships, prizes, lobbying) is valuable to your members then charge them a membership fee. There are new business plan ideas that can help wean your society from its subscription addiction. Check out Harvard’s Societies and Open Access Research (Retrieved June 24, 2019).

2. Conferences with more than 300 participants

This is not to say that you should not offer co-present events. If you are running a conference with a couple thousand members (or more, or many more), you are complexifying the central reason people have flown into your meeting: to find each other, and make new, or revive old, personal associations. Stepping into a big-city convention center with your colleagues may feel good, for about the first hour. After that it’s all random noise.

Your conference carbon footprint is inexcusable. You should respect and support members who pledge to not fly at all (See: should climate scientists fly Retrieved June 24, 2019). Find creative ways to split up large national/international meetings into a number of smaller meetings with better focus and a lot more interpersonal time.

3. Poster sessions with no digital archive

Posters are a good way to show off work in progress, and an opportunity for small-group interactions. They do take a significant amount of time to produce, so they deserve a permanent home on the internet. If the way you do your poster session is obsolete, you can fix that. Find an open online platform for poster sharing and use it well. And use better poster design requirements (Retrieved June 24, 2019; video here) to help start conversations.

4. Meetings dominated by plenary talks and sessions devoted to individuals presenting papers

Apart from a few plenary talks curated to help the room consider new technologies and findings that stimulate conversations, most talks can be recorded and posted online before the meeting, and not take up the agenda. Instead, do workshops and panels that provoke discussions and learning moments. Use conversation models (such as the world cafe <http://www.theworldcafe.com/key-concepts-resources/world-cafe-method/>) to bring together early and late career professionals. Long breaks, good food, nearby coffee houses, and beer also help.

5. No support for member collectives/clubs

A mailing list is not a “community.” You need to broadcast less and listen more. Your members need to find others working in very similar research arenas, and to have peer-to-peer online collaboration tools. Help them do this. Become research match-makers and coordination masters. That’s your new value proposition.

Are your association’s membership numbers a metric for you? Is bigger really better? Remember that the r/science sub-reddit <https://www.reddit.com/r/science/> has twenty million members. Theirs is bigger than yours. Go online and build active communication/collaboration resources for your members. There is a Deep Dive for this: Collectives, assemblages, communities.

6. No support for cross-disciplinary assemblages

Real innovation happens when people bridge between disciplinary/domain-specific silos. The least your society can do is open up its digital holdings to be indexed broadly by others. The clubs that your society supports need to find clubs in other societies to share their issues and problems. You can seek out venues and avenues for conversations (online or in person) with other societies. No society is alone in the global science endeavor.

7. Perhaps your whole learned society is obsolete

You may need to visit the “realm of chaos,” (See: The Work of Culture) at a board retreat somewhere, and rethink the entire purpose and vision for your society. If this society was created in a previous century to support a then-new sub-disciplinary journal, it may be a good time to pass this responsibility over to a pre-print repository and go home. Got some endowment money left over? Do a final meeting, make it free for graduate students and early-career folks, and challenge them to come up with the next best thing. Roll over the endowment to that. Got a big, fancy building for yourself? (Good for you!) Put it on the market, and use the funds to help all your employees find good jobs elsewhere.

Demand Sharing: a Real Sharing Economy for the Academy

In a famous letter of 1813, Thomas Jefferson compared the spread of ideas to the way people light one candle from another:
“He who receives an idea from me, receives instruction himself without lessening mine; as he who lites his taper at mine, receives light without darkening me.”

Share what’s important to you. Demand what you need. Photo Credit: Hartwig HDK on Flickr, CC By-ND 2.0

Demand sharing means you can ask for everything you need to do your science… with one proviso…

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

We’ve all heard about the “sharing economy,” where we can gain new streams of income or convenience by simply sharing excess capacity (that spare room, the car ride, an electric scooter, etc.). And we’ve been told since childhood that sharing things we no longer need can help those with greater needs. Most of us feel we have a good idea about what it means to “share.” But then most of us are also mistaken, and here’s why.

Anthropologists who look at the ethnographies (and who do their own) of hunter-gatherer groups, and who sometimes also look at modern attempts to create sharing economies (e.g., Uber, Airbnb, etc.), tell us several things about sharing that most of us may find new and different from what we expected (Widlok, 2016; Suzman, 2018 <https://aeon.co/essays/why-inequality-bothers-people-more-than-poverty> Retrieved May 6, 2019). These ideas about sharing, synthesized from the study of human groups that have been successfully building their own lives for tens of thousands of years, say to us that we have “sharing” almost completely wrong.

For example:

• Real sharing is not charity. Charity is an artifact of the marketplace (and of personal wealth) and the logics of artificial scarcity.

• Sharing something you own that you are not using (like a spare room or space in your car) in exchange for cash is just another form of market transaction.

• Giving away things that you don’t need or no longer want is not a good example of sharing. This is an edge case.

Demand Sharing: share what is most important to you. Get what you need in return

In this handbook, we use the phrase “demand sharing” to designate a culturally advanced form of sharing, a type of cultural behavior that has been in widespread use of the majority of the human population for tens of thousands of years, and only recently eclipsed by marketplace logics in the past two to three hundred years. “Millions of years of evolution have designed us to live and think as community members. Within a mere two centuries we have become alienated individuals. Nothing testifies better to the awesome power of culture” (Harari, 2014).

Society uses demand sharing to fund its needs

A rather good (perhaps unexpected) example of demand sharing in modern society is having your representative government demand a tax that everybody pays, which then, for example, supports your state’s public colleges and universities (and pays your salary). That’s right; taxation is how a society demands of itself those resources it needs to prosper (Widlok, 2016).

Another example is sharing within a household, where family members can grab a snack from the refrigerator without much bother or need to justify or account for their choices. In the case of the academy, the “refrigerator” is the rapidly expanding corpus of digital research objects, and the family is fellow scientists who stock this with the outputs of their work, and who can then dive in and grab what they need for their own research. Note: this is a never-empty refrigerator, as these digital objects are not used up by their taking. Note again: they are anti-rivalrous: they gain value when they are shared. This is something every open scientist needs to remember.

“[L]earning is taken as much as given” (Godin, 2019; <https://seths.blog/2019/05/college-confusion/> Retrieved May 6, 2019).

Learning is demand sharing for knowing

Teaching and learning already require demand sharing. As an open scientist you’ve probably taught in a variety of classroom situations. Your students asked questions to extend their learning. Your best students (bless them) outright demanded to be taught. They marched into libraries (buildings, or on-line) and demanded the resources they need. They came to your office hours and demanded answers to their quandaries.

This means that nearly every scientist is well versed on how to participate in a demand-sharing economy. First, the state demands that its citizens fund the university, supporting teachers and learning. Then the student shows up and demands to be taught. We all did this as students. It’s not obscure, it’s how we learn.

Imagine a professor giving a lecture who stops in the middle and says, “This next part is really interesting; if you want to learn it, go to my app on your phone and deposit $10.” This should sound bizarre to you (if it doesn’t, then the neo-liberal university is your real home).

The for-profit textbook industry is very close to this same idea, particularly when a professor assigns their own textbook.

In part it sounds strange because the professor’s salary is already paid, hopefully through taxes. But mainly, it sounds wrong, as professors (who were once students) are completely happy for their students to learn. These learning moments in the classroom are seen as socially important and personally rewarding. When a student asks you a question, you do your best to help them learn something new.

In the hunter-gatherer culture, when a child comes to your fire and asks for some bit of meat from your catch, you always give it to them. Like food at a hunter-gatherer fire, information in a college is something that can be demanded. Demand sharing in education is a type of cultural economy where the norms and rules — the times and places, the manner of asking, the desire to teach and the value of learning — are well-known, without being written down. Students know they cannot demand the answers to a quiz in advance. What is sometimes forgotten is the need for and role of kindness in these interactions; more about that a bit later in the handbook (Deep Dive: Kindness).

Got a PhD? You know how to demand what you need

This means that you already know how to do demand sharing. Let’s look how demand sharing differs with what we just described as poor examples of “sharing.”

• You don’t give your classes as a form of charity (even though you may consider your own salary inadequate). You are a professional. Teaching is important. Your students have legitimate demands on your knowledge and your kindness. Passing on knowledge is why you teach.

• You also don’t teach your students content that you find worthless to you or loan them books that you are no longer satisfied with, unless these books are instructive in other ways. You share what is really important to your professional life: the best knowledge you’ve acquired.

• You expect students (at least, grad students) to demand from you what they need to learn and grow as scientists.

Demand sharing means sharing what is important to your research

This is the proviso we mentioned above. The same demand-sharing logic that collects the taxes that pay your salary, and enables your students to learn, also enables the academy to manage its knowledge resources for the benefit of all scientists, and the planet through the internet. Until today, a scientist might legitimately point out the huge amount of process-friction that would overly complicate sharing her data or workflows. A lot of the work of open-science advocates in the last two decades has been focused on reducing that friction through web-based platforms and services. Much of the remaining friction is cultural; linked back to institutional practices that do not reward or actively punish open resource sharing.

In an open-science, demand-sharing academic culture, sharing as much of your research as early as possible is a virtue strong enough to be a norm. Share what matters most to you: your methods, your findings (even null fundings), and your data. Share your best ideas openly, not simply those ideas that you have no interest in pursuing and every interest in having someone else pursue (See also: Idea Farming). Share your knowing by listening and adding to the conversation.

Open science requires generosity with a simple promise: each scientist will get more than they give. That’s the bargain the academy makes with you when you join and actively participate in the open-science academic society.

Of course the whole push to reboot the academy is based on the premise that this bargain has been bent and broken in many places.

This bargain is bolstered by the network effects of academy organizations. Demand sharing optimizes this bargain across academic networks and clubs (Redaction, 2016; Retrieved June 1, 2019).

Sharing imbeds your work into the community of science as a gift, a form of offering that also signals your membership. Sharing includes reviewing and acknowledging the work of your peers (See also: Perils of peer review). The open-science community creates its internal authority through relentless self-critique.

This authority works through a special soft of reciprocity and a level field of mutual status. As Polanyi (1962) noted, “[O]nce the novice has reached the grade of an independent scientist, there is no longer any superior above him. His submission to scientific opinion is entailed now in his joining a chain of mutual appreciations, within which he is called upon to bear his equal share of responsibility for the authority to which he submits.” This reciprocal authority of “mutual appreciations”, based on openly shared and critiqued knowledge is the basis for all applications of authority and leadership in an open-science academy (See also: Leadership and sharing).

The offerings you provide to the “republic of science” (Polanyi, ibid) lend you the cultural capital to demand the resources you need for your work from the abundance of open-access resources, and the knowing of others in your field. These, in turn, offer up their research for your use. As Hyde (2009) notes, the “constant and long-term exchanges between many people may have no ultimate ‘economic’ benefit, but through them society emerges where there was none before”; your contributions help create the academy society.

Amplified by the internet’s global reach, these exchanges expand and accelerate the process of science. You share the most important ideas you have, even at the risk of being scooped, because getting the most important work done now — whether you do this or someone else does (and attributes you with the idea) — moves science forward. You share your research results, all of them, knowing you will be critiqued by your peers, as you will also critique theirs.

“The self-image of humans who are embedded in sharing relations is not one of homo faber who creates his or her world out of nothing and without anyone else. Rather it is an image of what I have called homo sumens … who takes into use what is available through the company of others and that can be claimed from them” (Widlok, 2016).

Academic clubs: collectives for research collaboration

Demand sharing is a dense cultural practice, with its own behavioral expectations. When you share, you signal your desire to be included in the community. What you must learn, then, are the guidelines for demanding resources. “[T]he problem is not one of deciding what to give to whom but rather what to demand of whom. The onus is on the potential receiver to make his or her claim acceptable and the rules for appropriateness are not about acceptable giving but acceptable demanding” (Widlok, ibid; emphasis added).

The cultural shift to demand sharing will create a social basis for new science collectives, for “clubs” that share internally as though the club were a single, social organism. These formations are not entirely new. R&D Think-tanks have been funded for this purpose, and the NSF in the US spends a billion dollars a year funding academic workshops to assemble temporary collectives to solve common problems. “Club goods” are non-rivalrous inside the club, but not necessarily without shared costs (Hartley, et al, 2019). Thomas and Brown (2011) describe these as well, “Collectives are made up of people who generally share values and beliefs about the world and their place in it, who value participation over belonging, and who engage in a set of shared practices. Thus collectives are plural and multiple. They also both form and disappear regularly around different ideas, events, or moments.” Collectives enable collaboration across the internet, inform team-building, and open up the cultural situations for shared knowing.

The cultural practices of demand sharing will be emergent in the academy as open resources — including access to and discoverability of collaborators — become increasingly available in the next decade. This Handbook will help you to kickstart your own collectives, and forge demand-sharing cultural norms that suit your situation; see also Building new collectives.

Together with “fierce equality,” demand sharing as a cultural norm can help realize an actual sharing economy for the academy, separated from the arbitrary scarcity of the neo-liberal marketplace; a gift economy grounded in mutual appreciation and reciprocity. The particular practices of demand sharing will need to grow inside thousands of institutions across the globe. A goal of this Handbook is to give you the resources you need to build demand-sharing logics inside your academy homes. You can be a demand-sharing culture change agent by sharing your research objects and your research questions and problems; by listening more and adding your knowledge when asked. Demand answers from others; learn together. It’s science, not alchemy. You are not alone.

References

Harari, Y.N. Sapiens: A Brief History of Humankind. Random House, 2014.

Hartley, John, Jason Potts, Lucy Montgomery, Ellie Rennie, and Cameron Neylon. “Do We Need to Move from Communication Technology to User Community? A New Economic Model of the Journal as a Club.” Learned Publishing 32, no. 1 (January 2019): 27–35. https://doi.org/10.1002/leap.1228.

Hyde, Lewis. The Gift: Creativity and the Artist in the Modern World. Vintage, 2009.

Polanyi, M. “The Republic of Science: Its Political and Economic Theory.” Minerva 1 (1962): 54–73.

Thomas, Douglas, and John Seely Brown. A New Culture of Learning: Cultivating the Imagination for a World of Constant Change. Vol. 219. CreateSpace Lexington, KY, 2011.

Widlok, T. Anthropology and the Economy of Sharing. Routledge, 2016.

Why Fierce Equality Matters to the Academy

“The Ju/’hoansi people of the Kalahari have always been fiercely egalitarian. They hate inequality or showing off, and shun formal leadership institutions. It’s what made them part of the most successful, sustainable civilisation in human history…” (James Suzman in The Guardian, October 2017 , Retrieved May 31, 2019). See Also: Ethnographic Note at the bottom of this essay.

“Open scientists in the academy are fiercely egalitarian. They hate inequality or showing off, and shun formal leadership institutions. It’s what made them part of the most successful, sustainable intellectual forces in human history…” Hopeful message from the near future.

This is Sue (true). She really loves open science (not as true). Fierce equality is universalism with teeth. Photo credit: Daniel Mennerich on Flickr. CC by-nd-nc 2.0

Fierce Equality

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

The academy needs equality, and not just the word. It needs normative, active, celebrated, fierce equality. It needs this first as a corrective to the twisted incentives of the past century of perversely accumulated advantage. It needs this as an open door for scientists in the south who have been locked out of conversations. It needs this to ground a new operating logic that does not permit the hiring of temporary faculty at penurious wage scales. It needs this to repair so many years of gender inequality. It needs this because the best science comes from a requisite variety of knowing that is all inclusive. Here we will explore this need.

The Academy Lacks Equality Today

The contrast between what fierce equality would look like in the academy and what you will find today, looking around your university, your discipline, your career (and those of your students), is probably striking. It was never supposed to be this way.

Science was meant to be rigorously inclusive. Merton (1942) used the term “universalism” to describe the foundational democratic norm of science (one of four norms, also the norm that most tended to be “deviously affirmed in theory and suppressed in practice” (ibid)). Universalism meant, and still means, that scientific discoveries can be made anywhere, by anyone. New discoveries are validated by the community (usually through replication). Their discoverers have equal standing in the “republic of science”(Polanyi, 1962) without the need for additional institutional or personal validation.

There are pragmatic constraints about proper methods and reporting that add a threshold to who is able to do and report science. But this threshold is, in theory, the same for everyone.

Cumulative Advantage

The suppression of universalism has several sources, including the external logic of neoliberal markets. Another factor is what Merton termed the “Matthew effect.” The Matthew effect describes all the ways that advantages accrue to a few individuals and are, simultaneously stripped from the rest. “Differences in individual capabilities aside, then, processes of accumulative advantage and disadvantage accentuate inequalities in science and learning: inequalities of peer recognition, inequalities of access to resources, and inequalities of scientific productivity. Individual self-selection and institutional social selection interact to affect successive probabilities of being variously located in the opportunity structure of science” (Merton, 1988).

Cumulative advantage has well-studied institutional and geographic features, which lead to advantages and disadvantages in hiring, funding, and publication. Despite a raft of entitled pronouncements to this effect, the academy is not a meritocracy; or else, it’s a terrible example of one (Morton, 2019 (Retrieved May 30, 2019); Standing, 2011; Emkhe, 2018 (Retrieved May 30, 2019); Way, et al, 2019; Harmon, 2018 (paywalled, Retrieved May 30, 2019); NAS Committee on the Impacts of Sexual Harassment in Academia, 2018). Academia is an informally reproduced aristocracy. It was never supposed to be this way; apart from the fact that it’s been this way for a long time. Which is why fierce equality matters.

Hyper-competitiveness (and funding)

Hyper-competitiveness at the institutional and personal level “crowds out” (Binswanger, 2014) science’s intrinsic motivations (including Joy and Passion) and promotes quantity over quality, “bad science” (Smaldino and McElreath, 2016), and marketable formalism over research needs. Worse, it crowds out scientists who refuse to play the bullshit-excellence game required by the gamification of reputation in the academy. Competition also feeds the Matthew effect: “[I]ntense competition also leads to ‘the Matthew effect’…this competition and these rewards reduce creativity; encourage gamesmanship (and concomitant defensive conservatism on the part of review panels) in granting competitions; create a bias towards ostensibly novel (though largely non­-disruptive), positive, and even inflated results on the part of authors and editors; and they discourage the pursuit and publication of replication studies, even when these call into serious question important results in the field” (Moore, et al, 2017). Science loses on all scores.

For science, hyper-competitiveness is a race to the bottom that so many institutions are fighting to win using arbitrary metrics as goals. “Competitiveness has therefore become a priority for universities and their main goal is to perform as highly as possible in measurable indicators which play an important role in these artificially staged competitions” (Binswanger, 2014).

Fierce equality and funding

Universities, funding agencies, and major foundations will need to construct new hiring, promotion, and funding practices that ignore ersatz excellence, pseudo-merit, and cumulative advantages. This process begins by envisioning how the outcomes of funding can be shared with equity across society, and then operationalize this vision. Refactoring hiring, promotion, and funding is the academy’s greatest need, and largest challenge, today. Changing the core logic for hiring, promotion, and funding will be a monumental task (Smaldino, et al, 2019). Failing this task, science will continue its race to the bottom. Tossing this task onto the shoulders of “open science” is perhaps unfair: this is a wider, deeper need of science and society (Newfield, 2016).

What fierce equality adds here is a new/old logic to anchor the discussions and decisions over what must come next. Like Merton, you can begin with the classic science norm of universalism; this time around it is vigorously affirmed in practice. You will find discussions on alternative research funding schemes and tenure solutions in other parts of the Handbook. As we learned in The Work of Culture, the academy will need to change behaviors to change attitudes, to change practices, to change research culture toward new ways (and sources and, hopefully, new amounts) of funding.

A closer look at fierce equality

What is “fierce equality” and how is this better than simple “equality”? You might note here that the Ju/’hoansi people, those hunter-gatherers who have practiced this for millennia, do not call their own cultural practices “fierce equality.” This is how anthropologists have captured the integral role that equality has in their cultural practices, and the tough behaviors that are used to maintain this. These highly visible, public cultural behaviors protect this shared norm against those within their group who are “bad actors” (See: Open Science: the Need for a Zero-Asshole Zone). Fierce equality is equality publicly defended at every opportunity where personal or group entitlement pops up.

Those who might argue that fierce equality would only work in small-scale cultural groups might want to reflect that most academic work happens in small-scale cultural groups (labs, departments, college faculties, teams).

Fierce equality means that open-science organizational behaviors: governance policies, rules, codes of conduct, plans for sharing and access to resources and to recognition, funding strategies, hiring practices, and face-to-face interactions are liable to be judged by how they promote equality within the global “republic of science.” Fierce equality operates internally in the academy (nobody expects the rest of the world to comply), and internally in all of the academy’s various organizations, each of which expresses this norm in their own self-determined governance. Every chapter in this book will talk about how open scientists can promote and perform fierce equality in their daily work.

As Michael Polanyi described the global academy in 1962: “The more widely the republic of science extends over the globe , the more numerous become its members in each country and the greater the material resources at its command , the more clearly emerges the need for a strong and effective scientific authority to reign over this republic . When we reject today the interference of political r religious authorities with the pursuit of science, we must do this in the name of the established scientific authority which safeguards the pursuit of science.”

Fierce equality is not a luxury. It is a long-term optimization strategy for the global republic of science; an expectation that emergent capabilities for sharing, mining, mixing, and reusing science objects can only realize their potential as a planet-wide, provident scientific resource when the entire community adheres (in multifarious ways) to the norm of equality. To build knowledge-maintenance organizations that are self-sustaining across decades and centuries of time, and for the whole of the global academy, there is no more fundamental principle than fierce equality. And there is no better time than now to refactor the academy using fierce equality as a foundational principle.

The academy as a gift economy

Fierce equality opens up contributions from across the world of science, and works at strengthening the “long tail” of discovery where real diversity spawns a massive variety of intelligences and promises innovation, discovery, fresh ideas, new knowledge. Fierce equality upholds the academy as an open gift economy, with its own logic of reciprocity.

An interesting tension that Hyde notes and resolves is how the academy uses knowledge (e.g., published papers) as gifts to offer status rewards, but does not actually attach this status to individuals as much as to the quality of their work and to their willingness to give this away to the scientific community. Any additional “prestige” attached to these gifts actually works against the interest of the global science community, and can be labeled a perverse effect on this.

As Lewis Hyde puts it: “A scientist may conduct his research in solitude, but he cannot do it in isolation. The ends of science require coordination. Each individual’s work must ‘fit,’ and the synthetic nature of gift exchange makes it an appropriate medium for this integration; it is not just people that must be brought together but the ideas themselves” (Hyde, 2009). You can do a Deep Dive into Gifting and Reciprocity later in the Handbook. What is important here is that “the academy” or “the republic of science” — whatever you wish to call the planetary endeavor for new knowing — needs to operate as a specific type of gift economy, using Demand Sharing as its logic, and fierce equality as a core norm.

Fierce equality does not mean “all ideas are equal”

Fierce equality is about equity of inclusion in academic life and work. It makes no claim about the relative qualities of the ideas introduced into the scientific conversation. All ideas are liable to validation and evaluations of their usefulness within their research domains. All findings are liable to interrogations of the methods and data that produced them.

Fierce equality is about erasing the dead weight of privilege, in exchange for open (as in to all, with additional recognition for contributions) knowledge collectives: cultural groups inside, outside (or both) of the current academic establishment. The goods of the academy will still be vetted; in fact, reviewed with greater transparency, fairness, and effectiveness than current peer review (Tennant, et all, 2016; see also Perils of peer review).

“Given the right opportunities, humans will start behaving in new ways. We will also stop behaving in annoying old ways, even if we’ve always tolerated those annoying behaviors in the past” (Shirky, 2010).

Applying a logic of fierce equality to your organization might present a variety of challenges. Your long-standing academic organization may have settled into any number of “annoying behaviors” that are defended as traditions, or simply as “the way we’ve always done things.” This Handbook is here to help you become a culture change agent, to kickstart the conversations that decenter pre-internet, pre-open science practices. Open science is here to offer a whole mix of “the right opportunities,” so your organization can do better things and stop getting better at doing obsolete things (Dintersmith, 2018).

Make a vision statement for fierce equality in your organization

A vision of the academic world operating though fierce equality is a thought experiment that many people in many academic organizations will need to do in the next decade. You and your colleagues can open up Culture Changing Activities beginning with statements about values and vision.

Here is one example of a fiercely equal, future-of-the-academy vision statement:

We envision an academy where members openly share their most important thoughts, processes, data, and findings through self-governing commons that are intent on the long-term stewardship of resources, on the value of reuse, on the absolute equality of participation, on the freedom of scientific knowledge, on open access to common infrastructures, and the right of all to participate in discovery and of each to have their work acknowledged, if not with praise, but with kindness and full consideration.

The particulars that inform this vision might include the following:

  • Widespread use of lotteries [Lotteries offer real solutions for democracy] for institutional or volunteer “leadership” positions (including department chairs and some deans), with initial terms of office fairly short (just long enough to evaluate performance) and opportunities for follow-on appointments (with limits). Good service is still noted and can be another source of informal recognition.
  • Badges [An Introduction to Badges] — when these are openly available to be acquired — can also be used as preconditions for entering lotteries. Want to be considered for dean? Take this badge MOOC. Skilling can be acknowledged and rewarded through badges. Badging also can become a primary task for professional associations/societies, as long as the ability to acquire the badge is not made exclusive [Against Exclusion: open is open to all].
  • The act of making one’s science work objects publicly available supports non-exclusive, anti-scarcity services: open repositories, pre-prints, idea farming sites, etc.
  • Career moments (promotion, job switching, etc.) are evaluated externally, and keyed to a record of active demand sharing and indications of non-assholish behaviors. Also, job applications have a layer of lottery (perhaps between an initial evaluation and the final decision). Implementing this is tricky and will require experimentation to optimize.
  • Lotteries are distributed into diversity buckets to be sure that the variety of selectees includes those who might otherwise be excluded.
  • Funding spread out to the long-tail of the community, with an ability to/requirement to also crowd-source the redistribution of some funds to promote work that is of widespread benefit.
  • Laughing at bullshit “excellence” and at the former desire to build exclusive academic “brands.” Remember it is possible to be elite, without being exclusive [Against Exclusion: open is open to all]. Remember “Harvard”? Remember “Nature”? Smile. Recognition shifts away from individuals and institutions and to the actual work and all the teams currently adding to this, and the long history of that work.
  • Nobel — and other — prizes honor ideas shared among networks (Keating, 2018). Lists of scientists across the planet who have contributed to a selected avenue of research might be assembled, mainly as a reference for future collaborations or historical records. Even as we might ridicule a government official for demanding gratitude when he was only doing his job, we need to start ridiculing those who want to claim personal credit for research results that a built on a wellspring of shared knowledge, teamwork, and luck. Deep Dive: Nobel Prize 2.0.

Ethnographic note:

Fierce quality was the advanced cultural practice system that informed potentially a majority of humans for tens-of-thousands of years.

“This research also revealed that the Ju/’hoansi were able to make a good living from a sparse environment because they cared little for private property and, above all, were ‘fiercely egalitarian’, as Lee put it. It showed that the Ju/’hoansi had no formalised leadership institutions, no formal hierarchies; men and women enjoyed equal decision-making powers; children played largely noncompetitive games in mixed age groups; and the elderly, while treated with great affection, were not afforded any special status or privileges. This research also demonstrated how the Ju/’hoansi’s ‘fierce egalitarianism’ underwrote their affluence. For it was their egalitarianism that ensured that no-one bothered accumulating wealth and simultaneously enabled limited resources to flow organically through communities, helping to ensure that even in times of episodic scarcity everyone got more or less enough.

“There is no question that this dynamic was very effective. If a society is judged by its endurance over time, then this was almost certainly the most successful society in human history — and by a considerable margin. New genomic analyses suggest that the Ju/’hoansi and their ancestors lived continuously in southern Africa from soon after modern H sapiens settled there, most likely around 200,000 years ago. Recent archaeological finds across southern Africa also indicate that key elements of the Ju/’hoansi’s material culture extend back at least 70,000 years and possibly long before. As importantly, genome mutation-rate analyses suggest that the broader population group from which the Ju/’hoansi descended, the Khoisan, were not only the largest population of H sapiens, but also did not suffer population declines to the same extent as other populations over the past 100,000 years.

“Taken in tandem with the fact that other well-documented hunting and gathering societies, from the Mbendjele BaYaka of Congo to the Agta in the Philippines (whose most recent common ancestor with the Ju/’hoansi was around 150,000 years ago), were similarly egalitarian, this suggests that the Ju/’hoansi’s direct ancestors were almost certainly ‘fiercely egalitarian’ too” (Suzman, 2018, Retrieved May 31, 2019).

References

Binswanger, Mathias. “Excellence by Nonsense: The Competition for Publications in Modern Science.” In Opening Science, edited by Sönke Bartling and Sascha Friesike, 49–72. Cham: Springer International Publishing, 2014. https://doi.org/10.1007/978-3-319-00026-8_3.

Dintersmith, Ted. What School Could Be: Insights and Inspiration from Teachers across America. Princeton University Press, 2018.

Hyde, Lewis. The Gift: Creativity and the Artist in the Modern World. Vintage, 2009.

Keating, Brian. Losing the Nobel Prize: A Story of Cosmology, Ambition, and the Perils of Science’s Highest Honor. WW Norton & Company, 2018.

Merton, R.K. “The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property.” Isis 79, no. 4 (1988): 606–623.

Moore, S., C. Neylon, M.P. Eve, D.P. O’Donnell, and D Pattinson. “‘Excellence R Us’: University Research and the Fetishisation of Excellence.” Palgrave Communications 3 (2017): 16105.

Newfield, Christopher. The Great Mistake: How We Wrecked Public Universities and How We Can Fix Them. JHU Press, 2016.

NAS Committee on the Impacts of Sexual Harassment in Academia, Committee on Women in Science, Engineering, and Medicine, Policy and Global Affairs, and National Academies of Sciences, Engineering, and Medicine. Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine. Edited by Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya. Washington, D.C.: National Academies Press, 2018. https://doi.org/10.17226/24994.

Polanyi, M. “The Republic of Science: Its Political and Economic Theory.” Minerva 1 (1962): 54–73.

Shirky, C. Cognitive Surplus: Creativity and Generosity in a Connected Age. Penguin UK, 2010.

Smaldino, Paul E, and Richard McElreath. “The Natural Selection of Bad Science.” Royal Society Open Science 3, no. 9 (2016): 160384.

Standing, Guy. The Precariat: The New Dangerous Class. Revised edition. London ; New York: Bloomsbury Academic, an imprint of Bloomsbury Publishing Plc, 2016.

Tennant, Jonathan P., Jonathan M. Dugan, Daniel Graziotin, Damien C. Jacques, François Waldner, Daniel Mietchen, Yehia Elkhatib, et al. “A Multi-Disciplinary Perspective on Emergent and Future Innovations in Peer Review.” F1000Research 6 (November 29, 2017): 1151. https://doi.org/10.12688/f1000research.12037.3.

Way, Samuel F., Allison C. Morgan, Daniel B. Larremore, and Aaron Clauset. “Productivity, Prominence, and the Effects of Academic Environment.” Proceedings of the National Academy of Sciences 116, no. 22 (May 28, 2019): 10729–33. https://doi.org/10.1073/pnas.1817431116.

Talking Principles, Values, Norms, Virtues, and Freedoms: A Primer on the use of terms

Kindness is a virtue in open science

“Nothing captures our understanding of moral commitment better than the way Marx astutely put it: ‘These are my principles; if you don’t like them, I’ve got others,’ (That’s Groucho Marx, in case you didn’t know)” (Benkler, 2011).

“Whether you’re designing a business model, a website, or a legal statute, values are not an afterthought. Fairness is not something you attend to after the practical decisions about how to improve efficiency or innovation or productivity have been made. Fairness is integral to effective human cooperation. We care about fairness, and when we believe that the systems we inhabit treat us fairly, we are willing to cooperate more effectively” (Benkler, ibid).

Values, freedoms and principles upon which to build new cultural practices for open science

PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.

For the open scientist, and for open-science societies and communities, statements of strategies, norms, and rules for open science are expressions of the principles, virtues, and values of open science. Before you can start to talk about open science, you and your colleagues need to figure these out. It helps to start with a shared sense of the meanings for these concepts.

Ambiguity warning: again, these words get used in various ways. Here you will find one way to fit this all together. You might prefer other ways, but at least, here is one you can use. Let’s unpack these a bit here, starting with values. Klamer (2017) introduces values like this: “Values are qualities of actions, goods, practices, people and social entities that people find good, beneficial, important, useful, beautiful, desirable, constructive and so forth. Values are personal in the sense that individuals experience them as such and they are social in the sense that values derive their impact from being shared among groups of people.”

Values can be internal only, or shared. Individuals can value anything they wish, but shared values require cultural work to sustain. Problems arise when there are contradictions between personal and cultural values. The values you hold as an open scientist do not need to be all of your values: you have lots of other values in your life. You might be highly religious, or deeply non-religious, for example. You bring these other values with you, and they help inform the discussion over the values you choose to share in your organization.

Norms are shared values that have become universal inside the culture of your community/group. Norms inform ways of behaving that members perform without much thought, and would feel weird if they didn’t do these. Norms are the basis for being able to say, “People like (us open scientists) do things like this.” Norms are culturally stronger than rules within teams. When people like us behave like this, you do not need rules to support these behaviors.

Principles (here) are a subset of values that appear to be unquestionable; a kind of super-value that might also be linked to fundamental meanings and connections to the world.[1] “Fairness” is a principle that is often articulated though values such as “equity.” The “open” part of “open science” is a value that is also a principle. Other values add facets of meaning to the principle of “openness.” “Open” also unpacks to contain other values: findability, accessibility, sharability, etc.. Building a list of values often reveals common principles that they share. Being “principled” (as a person or a community) means that you are true to your principles/values. There is a lot of semantic overlap between “principles” and “norms.” Norms describe the behaviors (including attitudes) that are informed by shared principles/values.

The Open Science MOOC has a whole module on open-science principles, as these have been articulated by several organizations. You can use these examples to create your own list of values/principles. But do create your own; then own these and celebrate them. In this work we point to two principles that serve to distinguish open science to non-open science: fierce equality and demand sharing. When these become norms, they might just be called “equality” and “sharing”.

Virtues to science by

“Prudence is a virtue, as is temperance, courage and justice. These are the so-called cardinal virtues that we find in the Nichomachean Ethics of Aristotle. Together with the theological virtues faith, hope and love, they constitute the seven classical virtues” (Klamer, 2017).

“Management is doing things right; leadership is doing the right things” (Drucker, 2001).

Virtues are values that have ethical meaning for you. These are not simply good to hold/do because they make sense; they are good to hold/do because they are the right thing. Virtues are not limited to just those found in books. You can articulate your own.

You can make a virtue from any value you hold as an ethical position. For example, dietary value choices might be virtues. “I would never eat meat” expresses a virtue, assuming you consider this an ethical decision. In contrast, other dietary choices might be aesthetic values (“I only drink single malt whisky”); or they can have a medical reason (“I’m allergic to peanuts”). These are not potential virtues.

A virtue that needs a lot of work in the academy is kindness (Deep Dive: Kindness). The idea that kindness might not be essential for the academy should be seen as bizarre. All learning happens through the kindness of shared knowing. The lack of kindness as a virtue has been linked to idealized hyper-masculinity (and the associated lack of ability/inclination to do emotional labor) (Schultz, 2002) and hyper-competitiveness. Both of these are toxic for the academy. If your organization is ignoring or violating its virtues, you have a real problem. Shared virtues, like other shared values, can, over time become norms in the culture of a community. People like us open scientists hold these virtues.

Open Science Freedoms

“It is our responsibility as scientists, knowing the great progress and great value of a satisfactory philosophy of ignorance, the great progress that is the fruit of freedom of thought, to proclaim the value of this freedom, to teach how doubt is not to be feared but welcomed and discussed, and to demand this freedom as our duty to all coming generations” (Feynman et al, 2005).

“Academic freedom” is larger, older, and more fundamental as a principle than the movement to open science. This freedom has also been abused in places (such as autocratic governments) and for purposes (neoliberal logics) that obstruct the academy’s defense of this, its primary principle. The fundamental nature of academic freedom was written into the Magna Charta Universitatum <http://www.magna-charta.org/magna-charta-universitatum> on the 900th anniversary of the founding of Bologna University, and signed by more than 700 universities across the globe.

Open science is another weapon in the defense of academic freedom. The pursuit of demand sharing promotes the free flow of research objects across nations; the shepherding of any/all research within sustainable repositories; and the demand for state support to maintain and improve these resources. The pursuit of fierce equality promotes wide access to academy resources, and inclusion of research findings from all persons.

What are the freedoms that open science brings to the academy?

Along with its values and principles, its standards and norms, open science may also include certain new freedoms similar to those presented by the open-source software movement. (See: The Free Software Definition <https://www.gnu.org/philosophy/free-sw.html> Retrieved May 15, 2019).

This brings up the question: is open science also “free science” (free as in “speech” not as in “beer”)? Since the scope of open science is available for debate and to local formations, there is no universal answer to this question, but there are some ideas that might inform these formations.

One leg of open science is “open access” to research objects. Peter Suber offers an excellent overview of this topic (<http://legacy.earlham.edu/~peters/fos/overview.htm> Retrieved May 15, 2019; see also Suber, 2012). He notes that the current push for open access does not require “universal access” in this, its initial moment. Today, open access offers an alternative to paywalled subscription access to academy resources. When you discuss open science with others at work, you will need to decide the scope of open access your organization would like to promote. So let’s explore this scope a bit. You will have your own conversations over freedoms as these are implied and supported by open science (or libra science).

Possible freedoms your open science endeavor can consider:

  • The freedom to access academy resources from anywhere. We do have the internet.
  • The freedom to interrogate the methods/data/software of any research result in the system. Access is a precondition of this.
  • The freedom to reuse academy resources.
  • The freedom to add to the academy’s corpus of research objects; subject to the rules of the repository applicable to all (e.g., provision of data).
  • The freedom to copy, mine, and analyze collections of research objects.
  • The freedom to be kind to one another in all actives of the academy.
  • The freedom to request help and receive kindness.
  • The freedom to participate equally in conversations, discussions, and online forums.
  • The freedom to always choose to do the right thing now, and not delay acting from your principles.
  • The freedom to point out infractions of community rules and principles without retaliation.
  • The freedom to express the joy of doing science and playing the infinite game.

Add your own freedoms to this list.

New behaviors will lead to new attitudes: build action into your culture change process

In building or changing the culture of your organization, the first, and an ongoing, task for you and your organization is to discuss and agree upon the values you want to share. The process of culture change in your organization begins with a discussion about values, then it builds statements that support these built as strategies, norms, and rules (See: Making statements about open science ). Then it looks at how things get done, at the practices that apply to getting to decisions and doing work, and realigns these behaviors with its shared value statements. After that, members of the organization continue to refactor how things get discussed, decided, and done, molding processes and behaviors to satisfy not just the boundaries of these values, but to express and defend their core principles. If you skipped The Work of Culture (above), you might want to take a look. Over time, these behaviors become shared norms. People like us open scientists here would not think of doing anything else. The whole process of how to do this is described below (See: Culture Changing Activities).

[1] “Scientific principles” are variously described as either the fundamentals of the scientific method, constraints on science (such as falsifiability) or very basic observations of nature (water seeks its own level). In casual use, the term sometimes overlaps with “laws”.

Photo Credit: “#staykind” by mikecogh is licensed under CC BY-SA 2.0

References:

Benkler, Yochai. The Penguin and the Leviathan: How Cooperation Triumphs over Self-Interest. Crown Business, 2011.

Drucker, Peter Ferdinand. The Essential Drucker. Oxford: Butterworth-Heinemann, 2001.

Feynman, R.P., J. Robbins, H. Sturman, and A. Löhnberg,. The Pleasure of Finding Things Out. Nieuw Amsterdam, 2005.

Klamer, A. Doing the Right Thing: A Value Based Economy. 2nd ed. London: Ubiquity Press, 2017. https://doi.org/10.5334/bbb.

Schultz, Vicki. “The Sanitized Workplace.” Yale Lj 112 (2002): 2061.

Suber, P. Open Access. MIT Press, 2012.

Open Science means leaving the idea desert for the idea farm

Open innovation starts with open idea sharing

“Less than 10 percent of innovation during the Renaissance is networked; two centuries later, a majority of breakthrough ideas emerge in collaborative environments. Multiple developments precipitate this shift, starting with Gutenberg’s press, which begins to have a material impact on secular research a century and a half after the first Bible hits the stands, as scientific ideas are stored and shared in the form of books and pamphlets. Postal systems, so central to Enlightenment science, flower across Europe; population densities increase in the urban centers; coffeehouses and formal institutions like the Royal Society create new hubs for intellectual collaboration.
Many of those innovation hubs exist outside the marketplace. The great minds of the period — Newton, Franklin, Priestley, Hooke, Jefferson, Locke, Lavoisier, Linnaeas — had little hope of financial reward for their ideas, and did everything in their power to encourage their circulation” (Johnson, 2011, emphasis added).

Ideas in the academy are another victim of the logic of arbitrary scarcity. They are also collateral damage in the proximity to the start-up economy. The academy should be an idea hot-house, instead we have an idea desert. The cultural shift to open science will be final when ideas flow across the planet like pinot at a faculty party.

Common-sense on idea sharing

Like talk, ideas are cheap. How many ideas (let’s limit these to synthetic insights about your area of research) do you have in a week? A day? An hour? So many that you really don’t take time to even jot them down? There are the ideas that connect your team (and the data you’ve collected, or plan to collect with funding) to a new hypothesis; that’s your next move in the infinite game. But so many other notions crowd into your thoughts:

  • Ideas you have while listening to a seminar talk outside your field, where you are curious how something you know might be of use or interest;
  • Ideas on where your discipline is headed, and those large-scale issues that might drive funder and association agendas;
  • Ideas that pop up when you read that new journal article (any article that does not give you new ideas is a waste of your time);
  • Ideas about what your graduate students might want to pursue to start their own infinite game play.
  • Ideas you put into that NSF proposal you submitted last month.

Face it: your professional life is brimming with ideas; that’s pretty much the point. Ideas are the starting line for the infinite game (See also: Things about science). There are about ten million science researchers on the planet. Each of them wakes up to a new day filled with new ideas. Almost all of them keep most of their ideas in their head until they are forgotten, replaced with other ideas, similarly forgotten, and a couple recent, unspoken insights.

Right now, today, almost all the ideas you have that are relevant to your work you might share if this were easy enough to do. You have any number of potential solutions for a wide range of issues in your area of research; solutions you have no intention of pursuing, but would really like to have solved — today if possible. For you, these are non-rivalrous ideas. You don’t mind if someone else, or anyone else, takes them and runs. At the same time, your idea might be a catalyst for someone else’s research; just the idea that leads them to a breakthrough.

You already have no reason to not share most of your ideas

Among your ideas are those you might want to propose to operationalize, given funding. So you tuck these away. And while your proposal is being evaluated, you worry that someone in that process will grab them for their own proposal (after down-grading yours: such is the sad state of the academy today).

The RFI idea paradox

Every so often, a funding agency or a foundation asks for feedback: they want your ideas about priorities for future research. Where will the science be in, say, five years? Ideally they would get a vast range of information from the thousands of scientists on their mailing lists. But realistically, they only get granules of ideas that are linked tightly to the goals of the teams/labs that will be angling for funding. “What should we focus on?” they ask. “Me,” you answer, although your text is designed to not say that directly.

Idea-gathering by funders is perhaps the least effective way to assemble knowledge about science. When it opened up an idea-farming platform to gather ideas, one major private foundation recently discovered that almost every idea came with an implied request for funding. So they shut down the service. This is not the fault of the researchers. They have five-hundred words to say what is most important for their discipline. What is most important for their discipline, in their perspective, is more support for an arena of research in which the researcher has already invested. Research funding is firmly embedded into the logic of scarcity today. Open science will explore other funding models.

So many ideas will not be collected by a funder RFI no matter how many responses they get.

The three elephants in the room for science idea sharing

Let’s recap here:

  1. Almost all your important ideas are non-rivalrous for you, so you have no reason to not share them, given the opportunity; and,
  2. Funding agencies are the least efficient organizations when it comes to gathering important ideas: the academy needs more and different idea gathering capabilities.
You already can share almost all of your ideas. 
 Funder RFIs are limited in utility; better to build an independent idea farm. 
 Your own ideas are good; but they are just the short tail of what is happening elsewhere. Add other ideas to them to make your ideas great, and then share these.

These are the two current “elephants in the room” for academy idea sharing. Should an open platform for science idea sharing (along the lines of current idea farming platforms) become available and popular, a third elephant is born: the possibility for open innovation. One of the major changes for corporate R&D in the past twenty years is “open innovation” (Johnson, 2011). This has also become a clarion call for the academy (Europäische Kommission, 2016). On the web, Quora and Stack Overflow offer networked question and answer platform solutions supporting open innovation. For-profit idea farming platforms like IdeaScale offer idea networking for corporate open innovation, through a subscription.

Open innovation starts with the premise “innovation happens elsewhere”:

“Innovation happens everywhere, but there is simply more elsewhere than here. Silly as it sounds, this is the brutal truth: Regardless of how smart, creative, and innovative you believe your organization is, there are more smart, creative, and innovative people outside your organization than inside” (Goldman and Gabriel, 2005).

Idea sharing for open science at an early EarthCube charrette

Ideas happen elsewhere too. The academy has a lot of elsewheres not often heard from. These “long-tail” communities and institutions are the academic homes to the great majority of scientists on the planet; they just happen to not be on the campus of one of the “better known” universities. The even longer tail includes scientists working outside of the academy, and citizen scientists anywhere. They all have ideas.

Put a lot of ideas into a shared, networked (databased, searchable, with discovery tools) environment, and innovation will blossom. This environment will become a place where, as Matt Ridley says, “ideas go to have sex.”

Connect even a million scientists (a small percentage of the total number) across the planet through their most recent ideas, and you should find a select few of them who happen to be considering precisely the same problematic currently puzzling you. Then you can reach out and build collaboratives to explore these together. Thinking of writing a proposal? Mine the combined idea farm of the planet to make your proposal ideas better; and then share these new ideas online (you can embargo them if you are worried). Your graduate students will be looking to see where their ideas are shared elsewhere, and how they can push their own infinite game play into new ground. You can mine the platform to sharpen your paper or your poster. Big data miners can also analyze and model these ideas to create a new form of synthetic learning about how science is done. The new elephant in the room is the potential capacity for communication of ideas, learning opportunities, and collaboration on the internet.

An open platform for your ideas to procreate: one good idea deserves a billion others

What if every day, say at the end of the work day, or after a beer, each scientist on the planet hopped online and added one idea to the global idea-farm platform (with some tags to help discovery)? What if ten-percent of them decided to add lots of ideas every month (the power-law curve suggests this is inevitable)? After a single year, there would be more than three billion ideas on the platform. Lots of overlap and similarities, but a whole lot of variety and difference too; coming from the minds of people who woke up in a hundred different nations. Each idea is time-stamped, with a permanent ID, and linked to its author. Every entry takes a minute or two to accomplish. A phone app lets you speak your idea directly into the mix.

Want to add a crazy good idea, or worried an idea might seem naive? Use your personal alias. Want to add a comment or a question to someone else’s idea? Go ahead. Feeling paranoid? Lock your proposal insight into an embargoed, timestamped vault on the platform. Open this later to show off. Then please try to be less paranoid, and more generous in the future.

Demand sharing means giving what is most valuable to you to the academy. This is a value and a norm for open science. Open science initiatives are building open platforms for a variety of internet services. The platform for open idea farming may not be here now, but can be built with a bit of funding and the right home.

Become an ImagiNative

There is a whole lot of “elsewhere” out there in the global Republic of Science (Polanyi, 1962). You need to be in touch will all these elsewhere ideas and with the people thinking them who also share your disciplinary/theoretical neighborhood. As Clay Shirky noted, “We also have to account for opportunity, ways of actually taking advantage of our ability to participate in concert where we previously consumed alone” (2010). You need to become an ImagiNative; open to new modes of collective knowing. And your lab, your school, your university needs to support open innovation and give up on patents (but that’s another blog). It’s all a part of playing the infinite game.

What if one of your ideas (you had this in the shower yesterday, and spoke it into your phone app over coffee) were picked up by a lab in another county, on another continent, and used to create a new theory that rocked your discipline; and in the paper that announced this theory, your idea was cited as a key element? How rewarding would that be? How many times might this happen across the planet in an open-innovation environment? And what if you searched the platform and found an idea from an early-career scientist in Sri Lanka that gave you a new insight into your current work, so you cited them in your next paper. How great for them. Soon, you might find the courage to give away insights you’ve been holding on to for years, and offering new ideas in response to the ideas of others. Congratulations, you a now an ImagiNative; a passionate knowledge explorer in the infinite game of science.

References:

Europäische Kommission, ed. Open Innovation, Open Science, Open to the World: A Vision for Europe. Luxembourg: Publications Office of the European Union, 2016.

Goldman, Ron, and Richard P. Gabriel. Innovation Happens Elsewhere: Open Source as Business Strategy. Morgan Kaufmann, 2005.

Johnson, S. Where Good Ideas Come from: The Seven Patterns of Innovation. Penguin UK, 2011.

Polanyi, M. “The Republic of Science: Its Political and Economic Theory.” Minerva 1 (1962): 54–73.

Shirky, C. Cognitive Surplus: Creativity and Generosity in a Connected Age. Penguin UK, 2010.