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)

 

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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.

Think of science like an incurable intellectual disease (Part 2 of 3)

Or, why you’re funding the right thing—the wrong way.

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Ideas aren’t the only things having fun at ESIP

Part Two: The NSF and NIH spent a billion dollars funding science workshops last year*, and all I got was a lousy white-paper.

Link to Part ONE

A little recap. In Part One we discovered that the most engaged groups online were not communities as much as they were collectives. Their engagement was already built-in because these groups were formed by individuals who shared life-threatening, or life-style challenging medical diagnoses. I then made an analogy to science, suggesting that we treat science like a life-style challenging intellectual diagnosis. The idea is that scientists who go online to do science are likely to want to create collectives rather than join online communities. I also mentioned that we still need community.

There is a larger story about science becoming hyper-competitive, and about the fear of being scooped if you share your data, and the whole neoliberal warping of the norms of science. I’m not going to delve into nor dispute this story here. Instead I am going to point out that significant scientific funding and scientist participation in collectives can already be evidenced in the activity of hosting scientific workshops to address important, shared issues. Science workshops are a major current expression of the value and need for science collectives. Workshops are where scientists gather in place to collectively respond to challenges they face in their research.

Like many of you reading this, I have travelled to and participated in several workshops over the past decade. I’ve met a lot of really smart people. Shared gallons of really bad coffee. Had more than a few beers after long, long days of somewhat-facilitated work. And I have spent considerable time helping write reports and white-papers. Most of these papers I never saw again. A few got published. Some workshops are more successful. Some are a shambles. I am currently planning a workshop (charrette) for next summer.

As a mode of collective science, there are times when a workshop makes perfect sense, and maybe always will. What I will propose below, however, is that there is a way to make the great majority of workshops unnecessary, by funding and building science communities instead.

Just as digital journal articles have acquired their granularity and an arbitrary scarcity based on the history of printed journals, workshops have acquired their own granularity and scarcity. Here are some of their limits:

  • Workshops need to have enough “work” to do to fill 1-1/2 to 2 days of effort (to justify 2 days of travel). You can’t do a half-day or, say, a twenty-day workshop;
  • Workshops need to support say 16-34 participants, and these scientists must be available at the same time;
  • Workshops get funded to explore science research topics “important” enough to justify their $40k budget.  Other collective issues and needs are not currently very fundable.
  • Workshops need to have a topic that is still an issue months after the proposal submission.
  • Workshops require moving people around in airplanes.
  • Some fraction of workshop proposals don’t get funded at all.

Workshops are a product of Twentieth Century science. Science before the internet. Science before someone figured out how to let scientists create their own collectives online at no cost. That’s right NSF and NIH funders; there is a way you can support thousands of self-organized online workshops with a net marginal cost of zero. Well… zero, that is, after investing about 20% of the current outlay for workshops to support several dozen self-managed science communities.

We can explore a working model for this Twenty-First Century strategy. Real lessons already learned and ready to be copied across other research domains. A model that already supports better, more effective, and more nimble collectives than the current workshop model.

One example we can explore today is ESIP

The working model here is the Federation of Earth Science Information Partners (ESIP). ESIP runs two community meetings a year, with funding from NASA, NOAA, and USGS. These meetings are based on member-submitted sessions, and offer ample time for informal networking. The meetings are intentionally held in places surrounded by restaurants, coffee-shops, and taverns. These occasions of physical co-presence are highly valuable. They are where ESIP builds its culture.

The semi-annual meetings offer enough face-time for community members to build the personal connections and interpersonal trust that can sustain hundreds of productive online interactions. Some members go to every meeting, some once a year, some every couple years. While a great amount of work is exhibited and done at these meetings—several workshops (from 1/2 day to 2 day) are also held at these meetings—they are also social gatherings of the self-governed community. Spaces of conversation. Places where, as Matt Ridley says, “ideas go to have sex.” The real work of ESIP happens when members decide to run their own workshop-like online groups called “clusters.”

Clusters are a model for the future of online science collectives. They have the virtues of being free, instant, active, and nimble (See: Appendix). They can merge with one another or diverge from their original intent as desired. They have no requirements for a deliverable, except that they reward the services of the volunteer time they spend. And so they are motivated to get real work done. Being surrounded by the much larger community that spawns them, they can grow to whatever collective size they need. And when their work is finished they disappear, leaving their findings in a discoverable location on the community wiki, and/or published in science journals.

The key to ESIP clusters is that they are grounded by a community that supports a shared vision and shared norms. This fosters teamwork that can better avoid becoming dysfunctional.  Not all clusters will accomplish what they originally intended. Some will accomplish much more than that.  ESIP has two dozen clusters running at this time. (Note to NASA and NOAA: that’s like running 24 workshops, which would cost funders about a million dollars to do independently.)  ESIP could support a hundred clusters without adding additional infrastructure. Note: the use of clusters as a form of science collective is a practice that is still open to innovation.

A while back I wrote a list of the returns on investment for funding community growth in virtual science organizations. I need to add this return to the list: fund and grow community and it will generate any number of science collectives that can accelerate understanding and innovation within that science arena.

In a pre-internet world, funding several thousand physical workshops a year helped fill some of the need for science collectives. In the future, internet-enabled science could be based on scientist-led communities that each spawn hundreds of active online collectives as these are needed. Imagine a couple hundred ESIP-style communities, funded at a million dollars a year each, and every community supports a hundred clusters. For a couple hundred million dollars, agency funders can get an equivalent ROI of their current billion dollar funding. The question is this: will new modes of internet-enabled science collectives (clusters) drive a change in the funding model?

Six more lessons learned:

  1. Cluster-like groups can become an important mode of online collective work across the sciences, with huge savings in time, money, and effort.
  2. When funders support travel to community-run meetings that grow a culture of sharing and trust, they enable these communities to host their own online collectives. Funders will save hundreds of millions of dollars by NOT directly funding workshops.
  3. Each additional cluster can be started with a zero marginal cost (based on existing support for backbone community organizational tools and services).
  4. Funders and community staff coordinate among these clusters to amplify the impacts of their results.
  5. Funders encourage cross-community online clusters for trans-disciplinary science.
  6. Funders can target some travel and other support to improve diversity at the community level. Staff work to nudge diversity at the cluster level.

Coming Soon: Part Three: Platforms and Norms: There’s a commons in your science future

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

*I’m just estimating here. I found about 5000 active independent NSF funded workshops listed on the website, and popped in an average of $40k each. I then doubled this to account for workshops organized inside funded projects, synthesis centers and networks. The NIH budgets for workshops are not so easy to pin down, but I’m guessing they are slightly higher than the NSF, since the overall budget is significantly higher. It would be great if I could get real numbers for all these. Not even counting NASA, NOAA, DOE, etc..

Appendix: Comparing Clusters to Workshop RFPs

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Think of science like an incurable intellectual disease

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Or, why your online science community engagement plans are probably wrong.

Part one (of three): It’s a collective, not a community, and that’s OK

Nearly a decade ago I was on a team that was exploring a new online network platform for ocean scientists—one of those “Facebook for X” forays that never took off. During the research phase I learned that online groups exhibited a wide range of “stickiness,” a description for member engagement. In general, engagement could be plotted on the usual power law curve; a handful of really engaged members on one side, and hundreds or thousands of mostly un-engaged members in the “long-tail” end of the curve.

One genre of online groups completely broke this curve. These were the most engaged groups online, and by a long ways. Their entire membership regularly contributed content. The problem—for them most of all, and for any online community manager trying to emulate their engagement on the open web—was that these groups were made of individuals who had been diagnosed with terminal or incurable chronic physical diseases.

These online groups, numbering in the hundreds, shared personal stories about symptoms and medication advice, uploaded and argued over new medical findings, and identified sources of emotional support for members and their families. They sought answers beyond the ken of their individual medical advisors, and they collectively shouldered the news when one of their members inevitably passed on.

The feeling of “community” was evident in their mutual concern, but this feeling was not central in these groups. “Belonging” was not the goal; it was their circumstance, their fate, their bad luck. Nobody was trying to get into these groups. Yes, they grew to care for and about one another. But they didn’t join for that purpose. Members joined because the circumstances of their lives brought them to this sad place: a space of collective struggle against a common and specific foe: their diagnosis.

Let’s explore the dynamics of these groups. Each online group focuses on a single disease or condition, from ADHD to Zika. Each member shows up already fully engaged in their own private struggle. What they need and find is an online collective, a place to share what would remain private in any other circumstance. A space of mutual learning. Douglas Thomas and John Seely Brown have described these spaces in their book A New Culture of Learning.  “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” (Pp 56-57). For more than a decade, the most engaged groups on the internet have been collectives, not communities.

The global internet has two virtues: it scales pretty will up to billions of users (e.g., Facebook); and it can host a hundred million independent groups. Online communities generally (and always when these are commercial in intent) love to grow bigger. Group size is a key metric. Belonging builds the brand. No company wants to say, “sorry, we don’t need any more customers at this point.”

On the very other hand, online collectives only need to grow to the size that optimizes the group’s collective intelligence and variety of knowledge. In fact, you know you’re in a collective when you try to join and somebody asks you what you bring to the conversation. Collectives have no long tail of non-participants. The collective may be very sensitive to an internal “signal-to-noise” ratio. The quality of participation is a feature.

To use another analogy (getting away from disease for a moment): if you joined a church congregation, you’re a part of that community, even if you only attend twice a year, and toss in a bit of coin now and then. But if you also join the choir, you enter a collective. Everyone in the choir is supposed to—you guessed it—sing. If you just stand there with your mouth shut, people will notice. If you don’t show up at all, someone will call you and ask where you are. There is no “long-tail” majority of choir members standing up in the choir loft not singing. The choir has zero need for a “choir engagement manager” to encourage choir members to actually sing. Singing is why members join. And if you happen to suck at it, others in the collective might encourage you to leave.

This leads me (finally) to science (including data science) and to the online engagement of scientists in social networks. From a series of cases and anecdotes collected from other community managers who have attempted to “engage” scientists online, it is clear that science effects its “victims” (scientists) much like an incurable (intellectual) disease. Scientists commonly spend sixty or more hours a week chasing unknowns in their labs, gathering field data, or tracking down software bugs. They share a fever for knowledge and their own common foe: the specific unknown that stands between the state-of-the-science in their specialty and a better understanding of the object of their study; the peculiar intellectual challenge (disease) they have chosen as their quest and their foe.

Scientists don’t need and don’t want to join online communities to do science. I am sorry, but if that’s all your new platform or service provides, your dance floor will remain empty. What scientists need are online collectives that can amplify and accelerate their own research, and reward their contributions to new knowledge in their chosen specialty.

Six Lessons so far:

  1. The most engaged online groups (at least in 2008) are collectives, not communities.
  2. Collectives don’t follow the power-law curve.
  3. Collectives form around specific issues, and common foes. They house a hunger for collective intelligence in the face of inadequate information. The driver here is a collective need to know.
  4. Unlike online communities, membership growth is not a desired metric within collectives. Small can be beautiful.
  5. In terms of engagement, science acts like an intellectual disease, a diagnosis of a specific lack of understanding about some object of study that drives the scientist to devote her life to discovery.
  6. Scientists will already be engaged if they join an online collective, and will already be disengaged if they are asked to join an online community.

Coming soon: Part Two: what the internet can really do for science.

Preview: The internet can provide is the capability of enabling millions of scientific collectives, linking these into a web of knowledge across the planet. It just hasn’t done this yet. We can fix that. Oh, yes. And why we still need community.

What are scholarly commons?

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I’ve just returned from the Summer ESIP Federation meeting, where we held a powerful discussion about the need for data commons (plural). This discussion got hung up a bit by a lack of clarity on the definitions of the terminology (including the word “commons”) and also a general lack of knowledge about the current literature on the commons (the group were mostly Earth data scientists).

So here I want to offer some short and very basic definitions (my own) and bring up some ideas and questions that might be of value to these discussions in the future. [I will also come back to this text  in the future and link to a bibliography that is just now being created by the Force11 team.]

Scholarly commons are…

Intentional communities (plural) formed around the shared use of open scholarly resources (a type of common-pool resource). Commoners work together as a community to optimize the use of the open resources they share. Scholarly commons are resource-near communities. They have an immediate and professional stake in the open resources they want to use. The whole community assumes a stewardship role toward these resources. These groups are self-defining and self-governing, each with their own emergent rules. Since scholarly commons are built upon open public resources, anybody on the planet can access them. When these are digital resources, they are not diminished by overuse. However, these resources cannot be sustained without the commons, or some other economy. These commons represent the social/cultural destination for any number of open-science efforts. (Note: Principles that can help all scholarly commons work together at the social level and as technical infrastructure are being considered at this moment in Force11.)

Scholarly commoners are…

Members of these intentional communities, with the freedoms and responsibilities that their communities provide and demand. Commoners work for the benefit of the whole community and for the sustainability of its open, shared scholarly resources. An individual commoner may belong to several commons. It is the role and the goal of commoners to help these open, shared resources flourish.

Scholarly commoning is…

The practice (and an attitude) that commoners bring to the scholarly commons. It begins with a logic of abundance, and depends on an active culture of sharing. Commoning is the activity to build and sustain the commons through shared practice (thanks to Cameron Neylon for this wording). Scholarly commoning is also imbued with an ethos of scholarship/science (however defined). Scholarly commoning informs how science can be accomplished through the use of open, shared resources (open ideas, open data, open software, open workflows, open-access publishing with open review, etc.) inside commons, instead of through other types of economies.

Other ideas/questions:

Can a single object in one open repository be claimed as a resource by more than one commons?

Scholarship needs to be fearless. One role of academic tenure was to protect this condition. In the face of the neoliberal market, tenure has failed in this role. Can the commons provide this protection?

Someone noted that many data objects are “uncommon” objects that require knowledge and knowhow to use and share. Scholarly commons also maintain knowledge and knowhow.

Someone said that the data commons might just be a thousand ESIPs, each one stewarding its own collections, optimizing their value, and creating APIs to share them. Sounds pretty good to me!  What does everybody think?

Using Patterns to Design the Scholarly Commons

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Force11 is looking to build an alternative academy based on a scholarly commons that supports the entire research to publication effort.

I just published a blog on the AGU blogspace.  Take a look here, or keep reading to get the gist.

Several groups (e.g., Force11 and theEuropean Commission) are calling for an integrative scholarly commons, where open-science objects—from ideas to published results—can be grown, shared, curated, and mined for new knowledge.

Building a commons is more complex than simply opening up objects to the public. The activity of commoning is what separates a commons from other examples of publicly shared resources. Research into the various commons found across the globe reveals that every successful commons is also an intentional cultural activity. And so, when open-science organizations talk about building a commons, they also need to consider growing a community of commoners.

How do we attain an intentional and reflexive cultural purview of commoning for science? One promising idea is to borrow from the open-access software community’s reliance on design patterns. Software design patterns reveal solution spaces and offer a shared vocabulary for software design.

A lexicon of design patterns could play the same role for the scholarly commons (See also: Patterns of Commoning). Since every commons requires a different set of practices suited to its peculiar circumstances, various commons within the academy will need to grow their own ways of commoning. The pattern lexicon would be expanded and improved as these scholarly commons emerge and grow.

Developing a pattern lexicon for the scholarly commons is an important and timely step in the move to an open-science future. Design patterns for a scholarly commons can reveal some promising solution spaces for this challenge, helping the academy make a transition from archaic print- and market-based models to commons models based on open network platforms.

Acknowledgements: Thanks to David Bollier for his contributions to this post.