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.

Open science organizations need to achieve the status of a Zero-Asshole Zone

Original found on Reddit

“First: the asshole helps himself to special privileges in cooperative life; second: he does this out of an intrenched sense of entitlement; third: he is immunized against the complaints of other people.” Aaron James: Assholes: a Theory… the intro video (2012) <https://youtu.be/d2y-pt0makw>

“An asshole is someone who leaves us feeling demeaned, de-energized, disrespected, and/or oppressed. In other words, someone who makes you feel like dirt” (Robert Sutton, 2019; from <https://www.vox.com/conversations/2017/9/26/16345476/stanford-psychologist-art-of-avoiding-assholes> retrieved April 9, 2019).

Open science organizations need to achieve the status of a Zero-Asshole Zone

Depending on who you talk to, the academy’s asshole problem is either extremely dire, vastly complicated, or both. Very few people would say it doesn’t exist. The “complicated” version tries to balance assholish behaviors with some idea that the pursuit of new knowledge in a hyper-competitive environment requires a intellectual with an enhanced sense of self-confidence, an enormous ego, a thick skin, and relentless drive. Only a complete narcissist can out-compete all the other assholes in the struggle for resources and credit. Colleagues who hang around this social-black-hole personality hope to ride along in the car of his success (i.e., “He may well be an asshole, but he’s our asshole”):

“The traits associated with narcissism explain why some people have an innate ability to dominate the scene. This includes the good serious face that implicitly tells their entourage that their research is important but also their willingness to use resources without any scruples or any sense of a possible cost for the community as a whole. This provides advantages in a system that monitors production and not productivity. We can understand why these innate leaders have supporters that praise their qualities — because of their fast-track access to resources that are usually difficult to get” (Lemaitre, 2015).

This “nice scientists finish last” mind-set serves to demonstrate why open science, with fierce equality and demand sharing, is an important, and urgent, remedy for the academy. The “cost” to the community — and to your own team, lab, department, or school — of even one real asshole is greater than you might at first guess. Assholes breed more assholes as they chase away nice, clever people. “Ultimately we are all diminished when clever people walk away from academia. So what can we do? It’s tempting to point the finger at senior academics for creating a poor workplace culture, but I’ve experienced this behaviour from people at all levels of the academic hierarchy. We need to work together to break the circle of nastiness” (emphasis in the original) (Mewburn, 2015). <https://sasconfidential.com/2015/11/09/niceness/> retrieved April 9, 2019.

You can argue ideas without being an asshole

It is really important here to understand that arguments over ideas are not intrinsically assholish events. As we will see below, assholes demean other individuals; their behavior is aimed at people. They will also be abrasive and demeaning in the manner in which they defend their ideas. We’ve all witnessed this in conferences and seminars. Entire paragraphs of meeting “code of conduct” rules are meant to counteract this kind of behavior. Sutton offers this: “enforcing a no asshole rule doesn’t mean turning your organization into a paradise for conflict-averse wimps. The best groups and organizations — especially the most creative ones — are places where people know how to fight” (Sutton, 2007).

Another complication the academy has is this: assholes with tenure. Sutton has no clear answer for this problem: “‘I’m with all these colleagues that are all tenured, and Stanford has no mandatory retirement,’ he points out. ‘So when I’m with an asshole, all I can do is hope’” (Sutton, 2017 <http://nymag.com/daily/intelligencer/2017/09/robert-sutton-asshole-survival-guide.html> Retrieved 4/9/2019; emphasis in the original).

“Science advances one funeral at a time;” Max Planck (1932/2015) had other, grand theoretical, reasons to say this. It also applies to assholes with tenure. So the best thing to do is: never hire an asshole in the first place. This is the essential message of the No Asshole Rule. No matter how much of an academic star she/he might be, adding him/her to your faculty is a huge mistake, even more so when they show up already with tenure.

In a corporate environment, you can just ask a high-powered jerk employee to go be a jerk in some other corporation. CEO coaches offer a simple principle: “‘genuine collaboration and accountability for our own actions are non-negotiable if you plan on succeeding in this place’. … Get this right [as a CEO], and you will set yourself up with a culture that delivers far greater and more consistent long term success than the short term spikes delivered by a Jerk!” (Francis, 2017 <https://www.linkedin.com/pulse/high-performing-jerks-culture-crushers-matthew-francis/> retrieved April 9, 2019).

Assholes in positions of power in your organization can be sidetracked as much as possible, isolated and ignored as circumstances allow. Graduate students can be warned away, administrators can be informed, and professional associations — where these assholes are eager to get into leadership positions — can be immunized through active word-of-mouth. Remember that a single asshole can impact your organization for years.

In The Problem with Assholes, Elizabeth Cullen Dunn announced that “Anthropology has an asshole problem.” She notes, “[a]ssholery is contagious. Once people see an asshole being an asshole and winning, actually gaining power and prestige by being an obnoxious self-interested bully, it creates a huge incentive for other people to emulate that behavior. Assholery has ripple effects as it spreads in the form of disciplinary norms that not only enable, but hyper-value nasty, elitist, demeaning behavior” (Dunn, 2018 <http://publicanthropologist.cmi.no/2018/06/20/the-problem-with-assholes/> retrieved April 9, 2019). Anthropology is not alone. In a 2018 report, the National Academies note: “In a survey conducted by the University of Texas System…, about 20 percent of female science students (undergraduate and graduate) experienced sexual harassment from faculty or staff, while more than a quarter of female engineering students and greater than 40 percent of medical students experienced sexual harassment from faculty or staff” (NAS, 2018). The asshole problem is acute across the academy.

Situational assholes

Sutton (2018 and 2007) notes that, on occasion, anyone can act assholishly. These “temporary assholes” are not the real problem. They tend to want to repair their lapses of civility, and to feel bad about their own behavior. The real problem comes from “authentic assholes.” A little later in this handbook we will talk about “dark” and “bright” core behaviors, (See: The bright and the dark) [NOTE: you are reading a draft of an essay in the Open Scientist Handbook, currently under construction]. This will allow us to unpack assholity into a small set of traits that can either be learned, or that display lasting personality disorders. Authentic assholes are also more likely to engage in an “exploitative sexual style” (Jones and Figueredo, 2013) that seeks instrumental sex with multiple partners; a trait that powers workplace harassment.

There is also a subset of assholes in the academy who are “accidental assholes” (Sepah, 2017 <https://medium.com/s/company-culture/your-company-culture-is-who-you-hire-fire-promote-part-2-anatomy-of-an-asshole-dba4f801b9f5> retrieved April 9, 2019). These are nerdish individuals who are, for example, on the autism spectrum, and who do not have the social skills to always act appropriately. They may do randomly assholish things, or they may simply copy the bad behaviors they find around them.

Not all assholes are born that way: lots of them are nurtured into bad behaviors on the job. The current, toxic academic culture can turn a temporary asshole into an chronic bad actor, a kind of “opportune asshole;” (or, in evolutionary culture terms, an “adaptive asshole”): someone who believes that bad behavior is expected of them and rewarded by their peers. They are happy to oblige.

This may be why so many precincts of the academy seem to be swarming with assholes (jerks, bad-actors, etc.). When you add the opportune- and temporary-assholes to the authentic ones, the numbers and their bad effects really add up. Sutton addressed this situation in an article in the Harvard Business Review (<https://hbr.org/2007/05/why-are-there-so-many> retrieved April 9, 2019). As the National Academies found, the most asshole-infested profession is medicine and medical school:

“A longitudinal study of nearly 3,000 medical students from 16 medical schools was just published in The British Medical Journal. Erica Frank and her colleagues at the Emory Medical School found that 42 percent of seniors reported being harassed by fellow students, professors, physicians, or patients; 84 percent reported they had been belittled and 40 percent reported being both harassed and belittled” (Sutton, 2007).

So, why are we surrounded by assholes? Sutton explains:

“The truth is that assholes breed like rabbits. Their poison quickly infects others; even worse, if you let them make hiring decisions, they will start cloning themselves. Once people believe that they can get away with treating others with contempt or, worse yet, believe they will be praised and rewarded for it, a reign of psychological terror can spread throughout your organization that is damn hard to stop” (Sutton, 2007).

The who is more important than the what

The good news is that the principles of fierce equality and demand sharing are diagnostic and therapeutic in finding and neutralizing assholes. Once the opportune-assholes find that their bad behavior is no longer applauded or even acceptable, they will need to self-monitor their personal interactions. When open-science norms support public acknowledgement of the asshole problem, and offer remedies for this in departments, labs, colleges, professional associations, etc.; authentic assholes will find that their toxic actions serve only to isolate and shame them (even though they may not feel this shame). Over time, when new norms take hold, and new hires bring fresh non-assholic voices into the mix, your corner of the academy can regain its fundamental civility, and you and your students can again argue theories and ideas, methods and experiments, without resorting to abuse and fear.

Working in a zero-asshole environment is significantly more pleasant and productive than toiling in the psychological minefield that even one asshole can create in your department, laboratory, agency, or college. Achieving a zero-asshole status takes a principled stance and procedural follow-through. It is a worthwhile goal for you as an open science culture-change agent to pursue. “Bear in mind that negative interactions have five times the effect on mood than positive interactions — it takes a lot of good people to make up for the damage done by just a few demeaning jerks” (Sutton, 2007).

The asshole in the mirror

A final thought here. Each of us is capable of astounding assholishness at any time. Most of us have experienced being on the receiving end on some occasions (in seminars, through peer review, at office hours) of abuse by those who control our academic fortunes, and use fear and humiliation in their critiques of our work, or of our capacities for research or teaching. We know how to do asshole; we’ve have enough training. We just need to not go there. And we need to isolate ourselves from the assholes we encounter. Sutton reminds us of this:

“If you want to build an asshole-free environment, you’ve got to start by looking in the mirror. When have you been an asshole? When have you caught and spread this contagious disease? What can you do, or what have you done, to keep your inner asshole from firing away at others? The most powerful single step you can take is to…just stay away from nasty people and places. This means you must defy the temptation to work with a swarm of assholes, regardless of a job’s other perks and charms. It also means that if you make this mistake, get out as fast as you can. And remember, as my student Dave Sanford taught me, that admitting you’re an asshole is the first step” (Sutton, 2007).

You can always take Sutton’s (2007) “asshole test” to self-diagnose. Or, if you find yourself believing that you are surrounded by idiots and that you should be recognized for your real talents and elevated into a higher level of society: you are probably an asshole, or at least, a “jerk”:

“Because the jerk tends to disregard the perspectives of those below him in the hierarchy, he often has little idea how he appears to them. This leads to hypocrisies. He might rage against the smallest typo in a student’s or secretary’s document, while producing a torrent of errors himself; it just wouldn’t occur to him to apply the same standards to himself. He might insist on promptness, while always running late. He might freely reprimand other people, expecting them to take it with good grace, while any complaints directed against him earn his eternal enmity” (Schwitzgabel, 2014 <https://aeon.co/essays/so-you-re-surrounded-by-idiots-guess-who-the-real-jerk-is> retrieved April 9, 2019).

This is a good reminder that assholes know who and what to kiss to get ahead. They may direct their assholocity at anyone/everyone equal or lesser than them in the academic scheme, and act entirely respectful and encouraging to those above them. Your dean may not know who’s an asshole, but grad-students might have a clear idea. Listen to them. And should you, in a moment of fatigue or stress lash out at your students, if you are a temporary asshole, then it’s up to you to make them know you acted poorly and regret it.

Feeling mean today? Go ahead, be mean to your data; interrogate it ruthlessly. Be cruel to your theories. Don’t look to validate them, find new ways to attack them. Be an asshole with your methodology; it’s certainly not as rigorous as it could be. Then, have some more coffee and be kind and humble with your students and colleagues.

References

Jones, Daniel Nelson, and Aurelio Jose Figueredo. “The Core of Darkness: Uncovering the Heart of the Dark Triad: The Core of Darkness.” European Journal of Personality 27, no. 6 (November 2013): 521–31. https://doi.org/10.1002/per.1893.

Lemaitre, Bruno. An Essay on Science and Narcissism: How Do High-Ego Personalities Drive Research in Life Sciences? Bruno Lemaitre, 2015.

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.

Planck, Max, Albert Einstein, and James Murphy. Where Is Science Going?, 1932.

Sutton, R.I. The No Asshole Rule: Building a Civilized Workplace and Surviving One That Isn’t. Hachette UK, 2007.

Things about science (that you may have not considered yet)

 

SUPER_Sq

Photo Credit: Tom Hilton on Flickr

Science

THIS IS a draft of an introductory essay for the Open Scientist Handbook… I would love to know if it’s going in an interesting direction.

There are books and libraries of books that talk about science: its history, sociology, philosophy, politics, and practice. As a scientist, you’ve likely gotten this far in life without reading any of these. You probably don’t need to start now. In this essay, a few remarks about science will help anchor the (still being written) Open Scientist Handbook into a particular framework for science as a project, as an endeavor, and a lifeway.

You are already a scientist, so you don’t need a general introduction to “science.” Also, you can learn everything you need about open science as a practice by checking out the Open Science MOOC.

WHEN THE HANDBOOK is done, this essay will have live-links into several other essays/sections in the book that you can explore if you wish, when it’s convenient. (NOTE: This handbook follows the “mullet” logic: all the great stuff up front, and the ragged details in the back.) Here you will find several Richard Feynman quotes. Do you want a good example of an open scientist? Be like Richard Feynman (who died before open science became a meme):

Feynman quote (still looking for the source):
“Physics is like sex: sure, it may give some practical results, but that’s not why we do it.”

Richard Feynman (from Wikimedia)

Science plays an infinite game because nature is the infinite game.

“If it turns out it’s like an onion with millions of layers and we’re just sick and tired of looking at the layers, then that’s the way it is, but whatever way it comes out, its nature is there and she’s going to come out the way she is, and therefore when we go to investigate it we shouldn’t pre-decide what it is we’re trying to do except to try to find out more about it” (Feynman et al, 2005).

Nature is not entirely knowable; for very good reasons, including its emergent, adaptive complexity, and our embedded place within it. Not yet knowing all about nature is why science still exists. Nature not ever being knowable is the scientist’s best job security.

Nature is a great part of what James P. Carse (1987) called the “infinite game.” By studying nature, scientists get to be players in/with this infinite game. Not many humans get to do this for a living, but all of us do this because we are alive. When we stop breathing, the infinite game goes on without us.

Carse has a list of distinctions between “finite” and “infinite” games. Francis Kane’s New York Times (04/12/1987) review of Carse’s book says:

“Finite games are those instrumental activities — from sports to politics to wars — in which the participants obey rules, recognize boundaries and announce winners and losers. The infinite game — there is only one — includes any authentic interaction, from touching to culture, that changes rules, plays with boundaries and exists solely for the purpose of continuing the game. A finite player seeks power; the infinite one displays self-sufficient strength. Finite games are theatrical, necessitating an audience; infinite ones are dramatic, involving participants.”

The point of playing the infinite game is to keep playing, to learn how to play better, and to add players to the mix; to sustain the game and the knowledge required to play this at its highest levels; to change the rules not to cheat, but to evolve and explore.

The infinite game goes on even when humans are distracted by the finite games they make up to give themselves victories to distinguish their efforts. The academy can choose to invest in playing the infinite game, or it can get distracted by finite games of manufactured scarcity, ersatz excellence, and accumulated advantage. This is where we are and the choice we need to consider.

Because nature is intimate with the infinite game, science cannot avoid playing this. Biological evolution, for example, is a theory that describes some of the adaptive and emergent possibilities of the infinite game. There is no end-point to evolution; no species really wins, some of them just have the chance to keep on playing. In fact, species extinction has a general positive effect¹ on the robustness of the ecosystem.

Complexity theories for the academy

Playing the infinite game is an intrinsically complex knowledge-management endeavor. Recent organizational management theories, such as the Cynefin Framework (started at IBM), warn that there are no “best practices” to deal with the “wicked problems” of adaptive complexity. This warning includes not just the marketplace, but also nature and culture. It turns out we are surrounded by emergent forces, and 20th Century management techniques are not up to the task.

While science methods have been addressing nature’s complexity for centuries, science knowledge-management and organizational governance have not kept up. It’s not hard to imagine science as an early-enlightenment project housed in late-medieval organizations. Open science looks to bring science governance into the 21st Century.

A bit on governance

This is an essay on science, not governance. Many of the sections of the Handbook offer governance guidance. Here it is only important to relate a couple major ideas.

First: your organization’s governance needs to be playing the infinite game. If your department, university, or research lab is still talking about “excellence,” or “we are ranked # X!,” or “the average salary of our graduates is Y$,” you are playing finite games. You need to stop that. You need to build infinite-game governance. Open science is here to help.

Second: organizations that play finite games against others playing the infinite game will always lose. The infinite game is a “long game.” Its players don’t care what other organizations are doing. They play to get better, not to win. Over time, they will out-innovate, out-think, and out-knowledge any peer who is chasing short-term finite wins.

Third: science is already positioned to play the infinite game; it gets funding from society (science goods are public goods); it holds a long-term privileged status within society; its “foe” (nature) is formidable and pushes science to ever greater tasks; its plan is flexible, it will reinvent itself as needed; its goal is just and grand: sharable knowledge of the universe.

To play the infinite game, however, science, and your workplace, needs one more thing: it needs you, and others like you, to step up and lead. You might want to take a look at the section (being written) Leadership in the Infinite Game to discover how you can lead your team, your lab, your school, or your agency in the infinite game.

Science has never been winnable. Nobody gets to figure everything out and finish science. Every bit of new knowledge is inextricably bound with a whole lot of other bits. It is a great example of the “long game.” Likewise, any bit of learning, every insightful thought or sentence delivered in your lecture, is fully dependent on a history filled with a whole lot of other learning moments: all of which turn out to be equally fallible.

Science wallows in doubt, devours unknowns, and shits little turds of incomplete knowledge

“When Socrates taught his students, he didn’t try to stuff them full of knowledge. Instead, he sought to fill them with aporia: with a sense of doubt, perplexity, and awe in the face of the complexity and contradictions of the world. If we are unable to embrace our fallibility, we lose out on that kind of doubt” (Schultz, 2011).

Science looks squarely into the unknown. A scientist is never as interested in the work she has already published as she is in the next unknown she is tackling in her research. Science’s knowledge-mignardises (or petit fours: sounds better than turds) can and have accumulated into important and useful — but still incomplete — facts and theories about our world and ourselves. And only science can do this.

Science is a “world-building” exercise; it strives to explain every-thing it contacts. There is no alternative world out there.² There are strands of complementary knowledges or untested theoretics that could use some investigation; there are “pseudo-sciences” like Astrology; but there is no alt-science world, not even in Reddit (we checked in March of 2019). The placebo effect shows we have a lot to learn about the healing process, but does not invalidate what we know.

The main adversary to science is bad science; open science looks to remove the (perverse) incentives behind most of today’s shaky research methods and results:

“[I]n science… it is precisely when people work with no goal other than that of attracting a better job, or getting tenure or higher rank, that one finds specious and trivial research, not contributions to knowledge. When there is a marked competition for jobs and money, when such supposedly secondary goals become primary, more and more scientists will be pulled into the race to hurry ‘original’ work into print, no matter how extraneous to the wider goals of the community” (Hyde, 2009).

Science rests on the possibility that everything it knows today is wrong. As Feynman noted: “Once you start doubting, just like you’re supposed to doubt, you ask me if the science is true. You say no, we don’t know what’s true, we’re trying to find out and everything is possibly wrong” (2005). Kathryn Schultz wrote an entire book on Being Wrong; science has a central spot in this work:

“In fact, not only can any given theory be proven wrong… sooner or later, it probably will be. And when it is, the occasion will mark the success of science, not its failure. This was the pivotal insight of the Scientific Revolution: that the advancement of knowledge depends on current theories collapsing in the face of new insights and discoveries. In this model of progress, errors do not lead us away from the truth. Instead, they edge us incrementally toward it” (Schultz, 2011).

Science makes no claim to be right, but every claim to be the go-to method that can find out if something is wrong. From there, it harvests knowledge that has not (yet) been shown to be wrong; this is as close to being right/true as there is. And scientists get to have fun by being less-wrong today than yesterday. Scientists are passionate knowledge explorers.

The joy of discovery needs a home in the center of science

“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. This is a very real and important point and one which is not considered enough by those who tell us it is our social responsibility to reflect on the impact of science on society” (Feynman et al, 2005).

Science is hard. It is the hardest ongoing task in all of humanity: after child rearing. One might expect society to honor, celebrate, and reward scientists for their labor. In the (not yet complete) Section on Joy and Passion you can discover more about how much fun you might be having right now as an open scientist.

For now, just consider that time spent playing with/in the infinite game can be intrinsically rewarding. In fact, it is potentially the most fun anyone can have. There is no video game, extreme sport, puzzle, quiz, theatre experience, or physical thrill that can compete with those moments you expand the edge of the planet’s knowledge envelope.

It is a privilege to be paid to spend your time in this pursuit. The privilege may not come with the type of salary/lifestyle society offers other occupations, but it does come with the freedom and the time to explore your own interests in nature/culture and the universe. This may be the best reason to keep the academy away from the logic of the marketplace, where freedom and time belong to others, where finite games fill your days and take you away from the very serious task of playing with nature.

Looking for the next patent, weapon design, or mass-consumable gadget or drug might make you rich, but it’s not science.

Using science resources and funding for science to accomplish these things, and their like, fits extremely well into the neoliberal logic of the marketplace. The incentives and rewards are nicely lined up. These finite games have obvious winners, and lots of losers too. Here is where the Matthew effecttranslates into cash rewards. Nearly all the current incentives for/in the academy have perverse consequences, including patents (See: Against Patents in the Academy[to be finished]). Marketplace counter-norms have already won, so it seems. Your “Research Excellence Framework” score matters a lot more than the actual new knowledge you and your colleagues have assembled.

This is why open science looks to build internal economies with its own logic, norms, principles, and rewards. There are lots of ways to be rich without much money; one key here is to manage your own expectations. Having “few needs, easily met” lets you locate a range of opportunities you might have overlooked. Here you might want to remember that open science is not just about publication access, it is about refactoring the academy to eliminate the sources for bad science, to accelerate the sharing of science objects across the planet, and to reboot the cultural DNA of academic organizations.

People will ask you, “how do you incentivize scientists to do the right thing […when the wrong thing pays off so well]?” You might respond by saying something like:

“How about giving scientists the means to do exceptional work, to have this work shared across the planet, to gather instant feedback from peers around the world, to live simply with plenty of time to do research without racing for funding, to have security of income and access to research tools.”

Time to do what you are passionate about is a great luxury, and has been for centuries. Setting your own goals, choosing yourself as the person who can contribute and accomplish great work, mentoring others to secure the future of science: these are incentives you can own.

Being a scientist is…

“Feynman always said that he did physics not for the glory or for awards and prizes but for the fun of it, for the sheer pleasure of finding out how the world works, what makes it tick” (Feynman et al, 2005).

At this point you might be thinking that the science described in this framework is not what you wake up and do every day. Your life may be dominated by demands from your organization for high productivity scores, funded research proposals, and publications in high impact journals; editors nudging you for your peer reviews; assistant vice chancellors pestering you with patent forms to fill out; constant rejections (curse you, reviewer three!) and revisions in your own output; courses to teach, lectures to prepare, and grades to give; and, right… home life. All this talk about joy and fun may seem oblique to your actual life.

Have hope. The high-pressure, low-fun career for scientists is not what science needs, and not how it was (and perhaps will not be again soon) designed to operate. Some decades ago, science was still considered a pursuit done best outside of the marketplace:

“[Vannevar] Bush convened a panel of leading academics to formulate a vision for postwar science policy. In July 1945, the panel produced a 192-page document dramatically titled Science: The Endless Frontier. Heralding basic science as the ‘seed corn’ for all future technological advancement, the report laid out a blueprint for an unprecedented union between government and academia — a national policy aimed at fostering open-ended blue-sky research on a massive scale. Though he was a conservative, Bush laid a groundwork for what Linda Marsa aptly termed a ‘New Deal for science,’ seeking to preserve a realm where university research was performed free of market dictates.

’It is chiefly in these [academic] institutions that scientists may work in an atmosphere which is relatively free from adverse pressure of convention, prejudice, or commercial necessity,’ wrote Bush in Endless Frontier, ‘Industry is generally inhibited by preconceived goals, by its own clearly defined standards, and by the constant pressure of commercial necessity.’ Of course there are exceptions, he acknowledged, ‘but even in such cases it is rarely possible to match the universities in respect to the freedom which is so important to scientific discovery’” (Washburn, 2008).

This freedom is what you’ve lost; what open science is determined to regain. You can find a lot of discussions around “academic freedom.” Being a scientist carries a great responsibility to maintain a specific variety of this. Again, here’s Feynman:

“It is our responsibility as scientists, knowing…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).

This “freedom of thought” extends to ideas shared freely within the academic community as gifts from scientists to the entire community. Hyde notes that this “gift” logic runs counter to the logic of the marketplace:

“A gift community puts certain constraints on its members, yes, but these constraints assure the freedom of the gift. ‘Academic freedom,’ as the term is used in the debate over commercial science, refers to the freedom of ideas, not to the freedom of individuals. Or perhaps we should say that it refers to the freedom of individuals to have their ideas treated as gifts contributed to the group mind and therefore the freedom to participate in that mind” (Hyde, 2009).

Being a scientist means giving what you learn, the best you have, to your peers in a sharing community, with the expectation that they will do the same. It is beneficial to remember that when your mother or grandfather was doing science, the academy’s position as external to the marketplace was valorized and celebrated. Being a scientist means you can demand the freedom, the time, and the resources to investigate your part of the infinite game: the object of your own study and your singular passion and potential joy.

“There can be occasions when we suddenly and involuntarily find ourselves loving the natural world with a startling intensity, in a burst of emotion which we may not fully understand, and the only word that seems to me to be appropriate for this feeling is joy” (McCarthy, 2015; see also: https://www.brainpickings.org/2018/06/07/michael-mccarthy-the-moth-snowstorm-nature-joy/).

Doing science is…

Science is the most difficult, most ambitious, most challenging pursuit that the human species has ever attempted. Every unknown is integrally linked to the entire infinite game that is the universe in which we swim. So your unknown — that bit of the game you have chosen to interrogate — is just as important as the next bit. Tackling your unknown is difficult by default (if it wasn’t this would already be a “known”). What is really painful is not being in constant, constructive contact with the five, or twelve, or a hundred other scientists somewhere on the planet who are, at this moment, running the exact same thoughts through their minds as you hold in yours.

Open science means you no longer need to consider these colleagues as your “competition.” A goal of open science is to connect you with these, your disciplinary siblings, and help you work faster, work better, and have more fun discovering more by working together than you can on your own. These are the people who can help you the most, and who need your expertise the most. Together you can make science stand up and dance in the infinite game.

Doing science means getting to play the infinite game for real. Doing science means unleashing your passion for knowledge exploration and diving into your research. Doing science means sparking the same passion for learning in your students. The role of open science in your life and for your research and teaching — and through the places where you work and collaborate — is to release you from manufactured scarcity, ersatz excellence, and the quest for accumulated advantage; from all of the finite games that others use to manage your life for their goals.

References

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

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.

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

McCarthy, Michael. The Moth Snowstorm: Nature and Joy. New York Review of Books, 2015.

Schultz, K. Being wrong: Adventures in the margin of error. Granta Books, 2011.

Taleb, N.N. Antifragile: Things That Gain from Disorder (Vol. 3). Random House Incorporated, 2012.

Washburn, Jennifer. University, Inc.: The Corporate Corruption of Higher Education. Basic Books, 2008.


[1] The infinite game is anti-fragile. This is another reason for its unknowability and another clue that it’s a long-game. Shane Parrish in the Farnam Street Blog <https://fs.blog/2014/04/antifragile-a-definition/> describes Nasim Taleb’s ( 2012) concept of “antifragility” this way:

“Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better. This property is behind everything that has changed with time: evolution, culture, ideas, revolutions, political systems, technological innovation, cultural and economic success, corporate survival, good recipes (say, chicken soup or steak tartare with a drop of cognac), the rise of cities, cultures, legal systems, equatorial forests, bacterial resistance … even our own existence as a species on this planet. And antifragility determines the boundary between what is living and organic (or complex), say, the human body, and what is inert, say, a physical object like the stapler on your desk.”

[2] Science is bounded to concepts/theories that can be “falsified”:

“Ever since the 1930s, when Karl Popper first argued for falsification as the main criterion for demarcating science from nonscience, the topic of “pseudoscience” has played an important role in the philosophy of science. Just because someone claims to be doing science or to be a scientist does not mean they are. Popper argued that if the theory did not put forth predictions that were “brittle” and potentially “falsifiable,” then they were not science. Theories that can be twisted post hoc to explain any kind of experimental outcome are not science” (Feist, 2006).

Commoning to share data, workflows, and results

SciData

This is the introductory talk I presented at the 2018 SciDataCon in Botswana.

Let me begin by saying how gratified I am to be here, and to see all of you, many of whom are unmercifully jet lagged, as I know I am.

I want to thank Mark Parsons for doing all the heavy lifting to organize this session, and I thank all the speakers for their hard work. We lost a few speakers when their institutions wouldn’t support international travel… This demonstrates a situation that local academics face every time they try to travel to conferences in the North. Anyhow, with fewer talks, we will have more time for discussion.

My talk is about commoning around data resources on a global scale. Commoning, I argue is the destination that open data and science deserves.

For more than a decade, open science advocates have been building the infrastructure and the cultural sentiment to support open sharing for science objects, from ideas, to work flows, to data, publications, and peer reviews, and to whatever comes next.

One vision of what should logically come next is a move to internally-governed academy commons. I use this term in the plural here, anticipating a great variety of these, where institutions, careers, and scientific research can be fostered outside of the global marketplace.

The exvestment of academy content, careers, and communication from the global capital marketplace will require numerous experiments in alternative markets and governance schemes.

In many ways, however, it also means a return to how science operated not so very long ago, only with new opportunities provided by the internet and subsequent technologies. We are looking at science as a public good — scientists produce real public goods too, in terms of new knowledge and a better informed citizenry.

We expect taxes will pay for this, and we can support the value of science to our governments in many different ways outside of capital-market based returns. That is why we now turn to building science commons.

Most of these commons will be localized experiments — localized, that is, through specific disciplines and their internal data resource needs, through the mosaic of academy institutions and repositories and their capacities for data storage and use, through agencies and funders with their need to advance specific science outcomes, and through a range of funded research endeavors where scientists collaborate between institutions and across national boundaries.

Ideally, these commons will be localized to foster cultural innovation based NOT on importing these ideas from the global north, but rather, beginning with local voices and local cultural issues in every corner of the planet. Science is science from Gaborone to Geneva. Out of this panoply of knowledges, capabilities, and visions, academy commons can be built and internally governed across the planet. This is the task ahead for open science.

We have to be clear that we are also talking about “data-near governance” for these commons: about ownership and stewardship by and for the individuals who really need these data, about collaboratives of scientists whose particular research depends on the long-term stewardship of specific shared data resources.

Collective ownership of the stewardship practices for these data will form the infraCULTURE and governance focus for international data commons in the academy. These governance schemes will need to be negotiated with the various repositories where the data are held.

In order for these commons to reinforce each other and so to build a planetary solution, they must also follow shared design patterns and interoperable cultural norms resulting in shared standards and principles.

These patterns and norms also inform the logic of commoning.

Look around today and you can see hundreds of newly fashioned open-science programs and software platforms being fashioned by a vanguard of scientists.

These are the launchpads for our shared cultural journey into the future of open science.

Here we are in Botswana. What a wondrous country this is. I was here some decades ago and I had the opportunity to visit some of its great natural preserves. If you buy me a gin and tonic some evening, I will tell you about the time I was stalked by a lion near Shakawe up on the Okavango…

Botswana also holds a special place in current theories of commoning and sharing economies. It turns out that AfroFuturism can be found not only in a fictional nation of Wakanda, but also in the deep, first-growth, hunter-gatherer cultures of Botswana and Namibia.

An advanced form of commoning can be found in the cultural logics of the sharing practices of traditional San societies in Botswana. Recent ethnographies by James Suzman and Thomas Widlok, for example, outline two powerful cultural norms found in traditional sharing economies that are significantly absent from today’s cosmopolitan, market-based sharing economies and services, such as Uber and Airbnb.

The ethnographies describe these norms as “fierce equality” and “demand sharing.” These norms, they claim, could productively inform modern sharing economies anywhere in the world; economies that can outcompete against Uber in the long-term.

Here I claim that these norms can help propel academic commons away from the perverse market incentives that currently intercept and corrupt the scholarly process. What Yochai Benkler calls “the tyranny of the margin,” the ratcheting up of ever larger productivity demands by the marketplace: this is the lion that stalks the whole academy. This is why we need to build commons and safeguard our practices with really strong shared norms.

What might these norms look like inside the academy?

Fierce equality puts the norm of equality first, at all levels of science. And yes, this is where #MeToo and #TimesUp enter into the heart of the cultures of science. But there is more:

Fierce equality will prompt significant changes to how societies, universities, and funders view and support the science endeavor. Fierce equality militates against what Cameron Neylon calls bullshit excellence and privilege in the academy, against the gamification of careers and reputations using external metrics, such as journal impact factors, and ultimately against all forms of the “Matthew effect” that amplifies inequality in funding and recognition.

“Demand sharing” takes “open” to its logical destination: every scientist on the planet has a need to find the resources that support her research. Any scientist should be able to demand their share. This demand is not automatic, however. It’s not some academy birthright. It doesn’t come with your PHD.

The cultural workings that support demand sharing also require that each scientist be open to sharing what is most valuable to her: data, of course, and findings, but also questions and concerns, pain points and critical observations, help for others as needed, and perhaps even kindness.

It’s interesting how difficult it is to consider kindness as a core norm for science. Why is that? I’ll leave this one hanging here… It’s another talk.

Injecting the norms of demand sharing and fierce equality into the cultures of the academy will require the widespread adoption of emergent intentional and reflexive cultural practices. Refactoring infraculture takes a lot of time and work.

Why should we bother? What do we get in return?

Here is one thing:

Science has already started the technological move from a logic of arbitrary scarcity and scarce data resources to a logic of resource abundance. This move is central to Fourth Paradigm science and the future of big-data use. The challenges of and the opportunities for a science based on data abundance is what brings us all here this week.

At the same time we build the cyberinfrastructure, we also need to build the cyberinfraCULTURE to grow the practices that support active sharing, mixing, mining and reuse of data and other science objects. Science will never achieve the full potential for resource abundance by clinging to exclusive property rights and building paywalls around science objects.

In some ways, the cultural future of science may look a lot like the ancient history of the peoples of Botswana. Their advanced knowledge of their surroundings has sustained them for tens of thousands of years. So too, advances in open science can sustain the global scientific endeavor into the future.

A vision statement for this future academy might be something like this:

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, 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.

We are all knowledge hunter-gatherers. Through open repositories, platforms and other cyberinfrastructures we are creating a provident big-data savanna that will nourish science across the globe. Through commoning cyberinfracultures we can teach each other to govern this savanna wisely. Wielding the norms of fierce equality and demand sharing, we can secure this future for all scientists.

And, with enough coffee, I think we might all make it through this day!

Thank you!

This talk was generously supported by the Alfred P. Sloan Foundation

About abundance in open science: Maybe your bucket is too big

 

bucket1

“If nature has made any one thing less susceptible than all others of exclusive property, it is the action of the thinking power called an idea, which an individual may exclusively possess as he keeps it to himself; but the moment it is divulged, it forces itself into the possession of every one, and the receiver cannot dispossess himself of it. Its peculiar character, too, is that no one possess the less, because every other possess the whole of it. He who receives an idea from me, receives instruction himself without lessening mine; as he who lights his taper at mine, receives light without darkening me. That ideas should freely spread from one to another over the globe, for the moral and mutual instruction of man, and improvement of his condition, seems to have been peculiarly and benevolently designed by nature, when she made them, like fire, expansible over all space, without lessening their density in any point, and like the air in which we breathe, move, and have our physical being, incapable of confinement or exclusive appropriation. Inventions then cannot, in nature, be a subject of property.” Thomas Jefferson 1813 letter. Quoted in (Boyle 2008).

How many Abundances does Open Science use?

We have not really begun to explore the many varieties of abundance that can emerge once we abandon arbitrary scarcity in open science. Primary abundance is built into digital science objects which, like Jefferson’s thoughts, can be copied infinitely without diminishing the original. Quite the opposite, the more copies that circulate, the more valuable the original object becomes, only not as the private property of an individual, but rather as a common pool resource for the science commons.

Combinatory abundance is what happens when science objects (and scientists) enter into a collaborative mode to mix, meld, and produce new objects. This is also where the network effect applies to objects, not just to people.

The difference between humans and animals lies in the ability to collaborate, engage in business, let ideas, pardon the expression, copulate. Collaboration has explosive upside, what is mathematically called a superadditive function, i.e., one plus one equals more than two, and one plus one plus one equals much, much more than three. That is pure nonlinearity with explosive benefits—we will get into details on how it benefits from the philosopher’s stone.” (Taleb 2012) paraphrasing (Ridley 2010).

Language is a good example of the kind of combinatory abundance that open science hopes to achieve through mineable/mixable repositories of a wide variety of knowledge objects.  The English alphabet has twenty-six letters and the English language about forty phonemes. From these all the words, sentences, paragraphs, texts and conversations are spun by combining and assembling them using rules and shared semantics.

You’re an academic, you know that academics might run out of ideas, or time, or even wine, but rarely do we run out of words. In fact this is one abundance that we have always enjoyed, perhaps a bit too much. To achieve the “explosive upside” of collaboration, scientists need to build open cultures of collaboration.

Emergent abundance describes the complex objects of study, the unknowns that feed science and also science’s willingness to not seek “truth”. Whether you are tracking the micro-second changes of a single cell or the collision courses of galaxies, you begin with a never-decreasing abundance of questions. Science also has an abundance of doubts, as well as discoveries. Science swims in an ocean of doubt, as Richard Feynman reminds us: “A scientist is never certain. We all know that. We know that all our statements are approximate statements with different degrees of certainty ; that when a statement is made, the question is not whether it is true or false but rather how likely it is to be true or false” (Feynman 2005).

What emerges from these doubts is a collective form of being only slightly less…wrong. Being less wrong iterates into being somewhat more right, but never to the point of actual truth. Everything we know today will be different from what we know tomorrow. “[S]cientists gravitate toward falsification; as a community if not as individuals, they seek to disprove their beliefs. Thus, the defining feature of a hypothesis is that it has the potential to be proven wrong (which is why it must be both testable and tested), and the defining feature of a theory is that it hasn’t been proven wrong yet. But the important part is that it can be — no matter how much evidence appears to confirm it, no matter how many experts endorse it, no matter how much popular support it enjoys. In fact, not only can any given theory be proven wrong; … sooner or later, it probably will be. And when it is, the occasion will mark the success of science, not its failure” (Schultz 2011).

Infinite abundance marks the recognition that science is not a finite game. There is no way to “win” science; no ending of science; and no possibility for its rules to be fully known; these are continually subject to change. The great mistake of bringing the logic of the marketplace (a finite, zero-sum game) into the academy is that it promotes behaviors that treat science like a finite game, and it makes competitors out of colleagues.

As an “infinite game,” science finds itself in a never-ending tussle with its objects of study; “Our freedom in relation to nature is not the freedom to change nature; it is not the possession of power over natural phenomena. It is the freedom to change ourselves. We are perfectly free to design a culture that will turn on the awareness that vitality cannot be given but only found, that the given patterns of spontaneity in nature are not only to be respected, but to be celebrated” (Carse 2011).

James Carse’s book on finite and infinite games offers a great heuristic for the type of culture change needed for science to become “open science.” 

“THERE ARE at least two kinds of games. One could be called finite, the other infinite. A finite game is played for the purpose of winning, an infinite game for the purpose of continuing the play.”
…“It is on this point that we find the most critical distinction between finite and infinite play: The rules of an infinite game must change in the course of play. The rules are changed when the players of an infinite game agree that the play is imperiled by a finite outcome—that is, by the victory of some players and the defeat of others. The rules of an infinite game are changed to prevent anyone from winning the game and to bring as many persons as possible into the play.” (Carse 2011)

Sufficient abundance reminds us that abundance does not need to be a waterfall into an overflowing bucket. As long as the bucket is full, there is abundance. A single extra drop makes it overflow. Abundance is relative to needs, and needs can be managed to the level of sufficiency, rather than expanded by market-fueled desires, manufactured from arbitrary scarcity:

“Scarcity is easier to deal with than abundance, because when something becomes rare, we simply think it more valuable than it was before, a conceptually easy change. Abundance is different: its advent means we can start treating previously valuable things as if they were cheap enough to waste, which is to say cheap enough to experiment with. Because abundance can remove the trade-offs we’re used to, it can be disorienting to the people who’ve grown up with scarcity. When a resource is scarce, the people who manage it often regard it as valuable in itself, without stopping to consider how much of the value is tied to its scarcity.” (Shirky, 2010)

Open science advocates are often asked about how they will replace (perverse) market incentives; as if these are the only incentives out there. Scientists have their own incentives, the reasons they are scientists and not, say, hedge fund managers. And scientists were fully incentivized in the decades before the marketplace intruded on the academy.

There are many articles about the mismatch between science and market incentives. A good place to start is Edwards and Roy (2016):  In this article, we will (1) describe how perverse incentives and hypercompetition are altering academic behavior of researchers and universities, reducing scientific progress and increasing unethical actions, (2) propose a conceptual model that describes how emphasis on quantity versus quality can adversely affect true scientific progress, (3) consider ramifications of this environment on the next generation of Science, Technology, Engineering and Mathematics (STEM) researchers, public perception, and the future of science itself, and finally, (4) offer recommendations that could help our scientific institutions increase productivity and maintain public trust. We hope to begin a conversation among all stakeholders who acknowledge perverse incentives throughout academia, consider changes to increase scientific progress, and uphold ‘‘high ethical standards’’ in the profession…”

Offer a scientist more time, cheaper tools, and some security to finish their research, and you will have a happy scientist. Chasing reputation points and writing endless proposals for funding would not compete with simply clearing the decks and letting research come to the fore. Managing needs can be a productive alternative to bulking up the CV with marginal publications. Open science can wean the scientist from perverse incentives by offering more with less.

Are you tired of working so hard to get just a bit more? One of the tasks of open science is to innovate to lower the costs of doing science. The most “successful” societies in the history of humanity became affluent by managing their needs:

“[Marshall] Sahlins characterized hunter-gatherers as the gurus of a “Zen road to affluence” through which they were able to enjoy “unparalleled material plenty— with a low standard of living.” Here, it seemed, was a people unconcerned with material wealth, living in harmony with their natural environments, who were also egalitarian, uncomplicated, and fundamentally free” (Suzman 2017).

Sometimes one can achieve abundance by simply finding a smaller bucket.

 

 

References:
Boyle, J., 2008. The Public Domain: Enclosing the Commons of the Mind. New Haven. Yale University Press.
Carse, J., 2011. Finite and infinite games. Simon and Schuster.
Edwards, M.A. and Roy, S., 2017. Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition. Environmental Engineering Science, 34(1), pp.51-61.
Feynman, R.P., Robbins, J., Sturman, H. and Löhnberg, A., 2005. The pleasure of finding things out. Nieuw Amsterdam.
Ridley, M., 2010. The Rational Optimist: How Prosperity Evolves. 4th Estate.
Schultz, K., 2011. Being wrong: Adventures in the margin of error. Granta Books.
Shirky, C., 2010. Cognitive surplus: Creativity and generosity in a connected age. Penguin UK.
Suzman, J., 2017. Affluence Without Abundance: The Disappearing World of the Bushmen. Bloomsbury Publishing USA.
Taleb, N.N., 2012. Antifragile: Things that gain from disorder (Vol. 3). Random House Incorporated.

Moving beyond community engagement for online science collectives

It’s time to support the passion of the scientist

Passion2

Some months ago I wrote about how scientists as a group on the internet behaved a lot like a certain class of groups; people who had been diagnosed with fatal diseases. The point of that essay was to illustrate that scientists have needs that go well beyond simple community. And I wrote it in part because I had been involved with several projects that had announced as their mission to create online communities for scientists, to develop strategies for promoting community engagement, or to train people to do this. As “community” can be described as a container for shared cultural practices, I can reaffirm that scientists really do need such containers in the process of reflectively reinventing the cultures of science. But they also need much more than communities to support their own quests to perform their science.

CommunityDatabase.001In organizational management theories, “community” (such as a “community of practice”), is useful for management as a tool to improve worker engagement, and it also makes workers more willing to share their tacit knowledge, which can then be recorded as institutional memory. “Engagement” in the corporate sense describes a positive emotional alignment of the employee with her work and co-workers. Engaged workers are said to be more productive (there is evidence for this), and so programs aimed at increasing their numbers have become routine. A somewhat more aggressive form of engagement is called “stakeholder alignment” which looks to build engagement for a specific project. This engagement helps projects move through implementation without hiccups.

“Community engagement” also extends the notion of engagement to customers or service users, in the drive for brand loyalty (in this case it’s also known as “customer relationship management”). At a time when customers have simple, powerful means to compare prices and ratings, forging a durable emotional alignment between the company and its customers becomes even more valuable. The same is true in the non-profit world where a new army of “community engagement managers” now works to keep donors loyal and their wallets open.

On the upside, the best community engagement programs support an open dialogue to improve the qualities of the workplace, or the product or service. There is a give, and not just a take here. On the down-side, the effort to promote engagement can entail a (seemingly) unending amount of emails or tweets or whatever, designed to remind workers or customers of why they need to be even more engaged.

Scientists show up at work or online already fully engaged… in their own research. They don’t need the offer of a group tour rate to cruise around New Zealand on a boat, nor another term life-insurance policy. What they need is to follow their passion: the passion of the scientist, of the knowledge explorer.

DUP402_Worker-Passion_vFINAL3John Hagel III has recently offered research suggesting—as I will show below— that scientists are actually unavailable to be engaged; that the community engagement efforts of professional associations and academic publishers will necessarily fail, and for a good reason. Perhaps for the best reason. Hagel’s argument is supported by a long-term research project he helped lead at the Deloitte Center for the Edge. See: Shift Index. See also: Unlocking the Passion of the Explorer.

Hagel notes that engaged employees or engaged customers are those who report they are happy with/in their current job, or with the current product/service. They have achieved a static form of satisfaction. From this disposition they can be relied upon to work harder or to buy more. After decades of thousands of corporate engagement programs across the US, only about 30% of employees (in their survey) self-report as engaged. The bulk of the remainder are unhappy for a variety of reasons. However, a few who are not engaged include those who come to work or to the marketplace following their own passions. Hagel is most interested in three passionate dispositions that he claims can add a lot of value to a company in today’s emergent economy, well above the return on any engagement program. Combined, these dispositions form what he calls “the passion of the explorer.” I would extend this description to include knowledge explorers: scientists.

Hagel (op cit) writes:

This form of passion has three components:

  • A long-term commitment to achieving an increasing impact in a domain
  • A questing disposition that creates excitement when confronted with an unexpected challenge
  • A connecting disposition that motivates the individual to systematically seek out others who can help them to get to a better answer faster when confronted with an unexpected challenge

That’s a powerful combination. People with the passion of the explorer are never satisfied or happy with what they have accomplished. What excites them is the next challenge on the horizon—it’s an opportunity to achieve more of their potential and take their impact in the domain to the next level. They are constantly seeking out those challenges and connecting with anyone who can help them address the challenge.

Passionate employees (in Hagel’s sense) are predictably unhappy with the status quo. Of course, an original meaning of “passion” is “to suffer.” They are necessarily immune from becoming engaged, and, I would guess, reactive to attempts made to engage them. In a 20th Century mode, these are not ideal employees. But the Deloitte study claims that these are precisely the type of employee needed for a 21st Century corporation.

In the academy, these are the scientists and the intellectuals who are passionate about their research, who are eager to teach others, and who are resource-aggressive for any new knowledge they can acquire. Attempts to improve their “engagement” in some form of community will find them refractory in the extreme. Gamification will leave them merely irritated. Emails to them will be deleted unread. The only community these scientists will really join, and then with some hesitation are those they own and manage by themselves for their own purposes. They are happiest when they can be connected to others who share their specific objects of study, and even there, their discussions point to unknowns and pain points in the research process.

Passion1How then can these passionate scientists be encouraged to connect, to coordinate their efforts, and collaborate online? What skills and knowledge do academic societies and universities need to acquire to move beyond engagement in order to unleash the collective intelligence of these scientists?

One model for such an organization is ESIP (Earth Science Information Partners). This year, ESIP is celebrating its twentieth year of supporting Earth science data use. The model ESIP uses is simple at one level but really complicated as it unfolds, because it is led by each and all of its member organizations and active science participants. Here are some ground rules that have worked well for ESIP.

The ESIP model for nourishing the passion of the knowledge explorer.

  1. Active ownership by the members, not by some board or background institution.
    Members determine the long-term goals and immediate activities. Each member is a CEO of ESIP. ESIP focuses on Earth science data. Each member can bring his/her passion for their part of this domain to the table. ESIP supplies the table.
  2. Ultra-low-threshold for participation in real-time science collectives.
    ESIP calls these “clusters”. Any group of members can call a new one into existence in a day or two. ESIP can handle up to fifty clusters at a time (more than this and the calendar gets ugly). Members are challenged to bring their full knowledge and demand the same of others. There is a lot of complexity here; clusters variegate according to the needs of their members.
  3. On-line asynchronous collaborations as the norm.
    We have this thing called “the internet.” No need to fly people around for workshops, unless this makes really good sense to do.
  4. Two actual meetings a year, with an emphasis on social interaction and interpersonal time.
    These are where ESIPers become friends and learn to laugh together. No papers are presented. Breakouts are for information sharing and learning. Networking is intense at ESIP meetings. With several thousands years of Earth data experience in the room, it’s the best place on the planet to get connected to others who have similar problems or interests. Two meetings a year keep the whole group more active throughout the year.

Open and Equal Underneath

Underneath all of this activity at ESIP is a total commitment to being open: open, transparent self-governance, open research objects, open sharing of knowledge and problems. Also apparent is an appreciation for each member’s needs and contributions. Early career and late career scientists engage in active conversations that can lead to new collaborations. In my next blog, I’ll discuss how open sharing and fierce equality can support new/old cultural norms for science.

Think of science like an incurable intellectual disease (Part 3)

welcoming-new-members
ESIP welcomes first-time meeting goers

GO TO PART ONE if you haven’t read it yet…

Part 3: Platforms and Norms: There’s a commons in your science future

Science is broken: Who’s got the duct tape and WD40?

So, here we are, Act III.

Act I was all about how personal science is. Scientists are individually infected with their own science quest. Act II was about how social science is. Why else would they take a hundred-thousand airline flights a year to gather in workshops and solve problems together (well, apart from the miles)? Act III needs to be about culture and technology. But not so much about the content of culture and the features of technology. Rather, about the doing of culture and the uses of technology.

Yes, the sciences are broken. Some part of this rupture was built-in (Merton, who outlined scientific norms in the 1940s, also outlined the integral tensions that disrupted these—i.e., the Matthew effect). But much of the damage has come from the displacement of the academy within society that has warped the culture of science.

Yochai Benker generally describes the tensions of this warping as “three dimensions of power”. These power dimensions (hierarchy, intellectual property, and the neoliberal need to always show more returns) work against science as a mode of peer production that self-commits to shared norms. Science needs to find alternative means to fight hierarchy, share its goods, and own its own returns.

The sciences are stuck and fractured, in need of both WD40 and duct tape—culture change and technological support. Scientists need to operationalize open sharing and collective learning. For this, they must discard the institutions that enable the above dimensions of power in favor of new communities and clubs (in Neylon’s sense of the term) that can house cultures of commoning, and activate global peer production.

At a recent workshop where the topic of the “scholarly commons” was the theme, I was again impressed by descriptions of how these dimensions of power are locally applied in academic institutions across the planet. The workshop was designed to arrive at a consensus on a universal statement, a short list of principles, such as a restatement of Merton’s norms. Instead, the organizers were reminded that these so-called universal principles could only be accepted as suggestions. These would need to be locally reexamined, reconfigured, reauthorized and only then applied as needed against the institutional cultural situation at hand. Here is another look at the dynamics of that workshop. 

Earlier in the Summer, I attended a breakout session at the ESIP Meeting where a long discussion about building an Earth science data commons concluded that ESIP was either already one, or ready to be one. A second determination was that ESIP was about the right size for this task, that multiple data commons could be built across the academy on the model of ESIP, but with their own sui generis culture and logic of practice, geared to local conditions and particular science needs.

The real question is not how to create the scholarly commons, but rather how to rescue (or re-place) current academic institutions using commons-based economies, and using the various norms of commoning as a baseline for the shared cultural practice of open science. The real task is then how to help move this process forward.

If commoning is the WD40 to release science for the sclerotic hold of its 19th Century institutions (Side note: Michelle Brook is assembling a list of learned societies in the UK. This list is already has  more than 800 entries), technology is the duct tape needed to help these hundreds and thousands of commons communities work in concert across the globe. The internet—which science needs to find out how to use as a lateral-learning tool at least as well as the global skateboarding community already does—holds the future of science. Shared community platforms, such as Trellis, now under construction at the AAAS, or the Open Science Framework, from the Center for Open Science can help solve the problems created by a thousand science communities supporting hundreds of thousands of clusters (collectives) needing to discover each others’ work in real time.

For commoning to gain traction in the academy, we must first explore this as a generative practice for open science. But as each commons spins up its own variety of commoning, we need to avoid prescribing universal norms for them. Instead, the most productive next step might be to unleash a more profound understanding of the circumstances of scholarly commoning by building a set of design patterns that will be localized and applied as needed to yank local institutions away from hierarchy, intellectual property wrongs, and the pull of the margins that preempt ethical decisions and norms.

Next summer, the ESIP Federation is hoping to host a two-day charrette at its Summer Meeting in Bloomington Indiana to begin the process of building scholarly commons patterns. A pattern lexicon for scholarly commoning will potentially help hundreds of science communities self-govern their own open resources and commoners.

Lessons learned (Parts 1-3):

  1. Science is intensely personal. Scientists are already engaged in their own struggle with the unknowns they hope to defeat. Their intellectual disease is fortunately incurable.
  2. Science is already social. Just in the US, several thousand workshops a year evidence the scientific need/desire to build collective knowledge.
  3. Science is cultural. Self-governed science communities can use intentional cultural practices to help scientists prepare to work together in virtual organizations with shared norms and resources.
  4. Community opens up arenas for online collaboration. Instant collectives, such as ESIP clusters, can replace expensive workshops and enable scientists to share knowledge and solve problems.
  5. These communities need to consider themselves as commons to replace institutions that have been twisted by the three dimensions of power (hierarchy, intellectual property, and neoliberal economics).
  6. Each commons needs to work locally, attuned to its local situation within science domains and academic institutions.
  7. The academy needs to harness the internet and technology platforms to knit together localized science/data commons into a global web of open shared resources and collective intelligence.