“So it’s kind of like if your house catches on fire. The bad news is there is no fire brigade. The good news is random people apparate from nowhere, put out the fire and leave without expecting payment or praise. …I was trying to think of the right model to describe this form of random acts of kindness by geeky strangers. …You know, it’s just like the hail goes out and people are ready to help. And it turns out this model is everywhere, once you start looking for it.” Jonathan Zittrain, Ted Talk 2009 <https://www.ted.com/talks/jonathan_zittrain_the_web_is_a_random_act_of_kindness?>
PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon. All comments are welcome! Revised from a talk at the ESIP 2019 Summer Meeting. With travel support from the Alfred P. Sloan Foundation. Arigatou!
There are lots of ways that the rational, logical, hyper-competitive, winner-take-all, zero-sum, prisoner’s dilemma, nice-guys-finish-last, single-bottom-line, annual-productivity ratchet — or add your adjective here — mindset is just wrong for sustaining the academy and bad for science. For decades now, the same neo-liberal economic schemes that have been used to reshape how governments budget their funds have also made dramatic and disturbing inroads into university budgets and governance. Open science can show how that trend is a race to the bottom for universities. What do you say, we turn around and go another way?
“I have learnt silence from the talkative, tolerance from the intolerant and kindness from the unkind.” Khalil Ghibran, Sand and Foam.
A century without kindness: the impact of external logics
The banishment of kindness as a necessary part of being an academic, — just one more feature of adopting the neoliberal marketplace logic, and another effect of hyper-masculinity in the workplace — allows academics to defer judgements about kindness:
“We want to argue, however, that although kindness is a commonplace in pedagogical encounters, easily recognisable by its presence or absence, attending to it can be subversive of neo-liberal assumptions that place value on utility and cost above other human values” (Clegg and Rowland, 2010).
The word for kindness in Latin is humanitas: kindness makes us human. “[T]he Roman Emperor Marcus Aurelius, a leading Stoic philosopher, speaks of kindness as ‘mankind’s [sic] greatest delight’ (Phillips and Taylor 2009, 18). In Aristotle’s teachings, kindness is a component of phronesis: an entire type of “practical wisdom” that we’ve slowly devalued over the past 300 years (Juarrero, 1999) [ and you can blame Hume and Kant and all the usual suspects for this]. Phronesis combines virtue with a notion of adult comprehension: a way of knowing the right thing to do in all circumstances. It has little to do with intellection, and everything to do with broad experience and learning.
The road to a doctorate is long and difficult, and so adding another layer of learning to the process might seem short-sighted. And yet avoiding learning phronesis in your daily life is probably not any easier than practicing this, since the absence of phronesis leads to serial mistakes in moral and practical judgement, any one of which can be “career defining” in a negative sense. “Practical wisdom” is integral to “doing the right thing” while you learn to “do the thing right.” Doing the right thing often includes knowing how to exercise kindness with others.
A child can show kindness, and we welcome this. An adult (one who has learned some phronesis) can act kinder than a child, because this adult is experienced in a broader range of social circumstances and personal relationships. An adult can be — to use the Yiddish — a mensch. And a mensch can be kinder than a non-mensch or a proto-mensch. Lesson: be an open science mensch.
Kindness starts with intention
Real kindness begins with a clear intention. This adds an important aspect of self-judgement to its base. Without this aspect you cannot actually be kind, even if others might interpret what you are doing as being kind. How do you actually judge your intentions, particularly in relationships with other people and things? Something to contemplate. Also note: Clegg and Rowland (2010) remind us that kindness is not equated with leniency or “being nice.” Real kindness uses courage to articulate accurate observations and open learning moments that can be difficult and painful for both parties.
Kindness is something you learn and do
Kindness is a normative human practice in a wide range of social frames: parenting, friendship, governance, teaching, caregiving, civil interactions. Zittrain (above) reminds us that the internet was built on kindness and generosity. In nearly every human social endeavor, kindness matters. Even in highly-competitive sporting events, “sportsmanship” is highly valued, and is actually an internal normative form of kindness. Why should kindness, and critical interrogations about its role, be absent from research and management in the academy?
Like rationality, kindness is a form of practice, not an emotion. You can no more “feel” kind than you can “feel” rational. Unlike rationality, kindness necessarily involves others, their perspectives and needs. Kindness can and will also be judged by others for its qualities. Is it genuine? Is it motivated by a need to be perceived as kind? Is it effective in performing its intention? What is its intention? In the academy where intellectual judgements run wide and deep, kindness opens up another opportunity to be judged. But so does being unkind. Or it should. For decades, the lack of kindness in our research institutions and workplaces has gone unremarked. It is time to remark these.
Culture provides meaning to intentions
Again, kindness begins with intention. The same activity with different intentions can be a kind, caring conversation, or it can be a cruel interrogation. Intentions are themselves colored by culture. Culture provides a layer of shared meaning/learning that helps the individual (both the intend-er and the intend-ee) discover and interpret shared meaning as intended. You and the other person can answer the question: what did you mean?
The social world always contains this layer of culture. There is no society without it. Individuals hold this layer as a shared/learned resource. The cultural values (See: Values, freedoms and principles) you bring to your open science organization can assemble the meanings that add clear intentions to shared kindness. Just as some institutional cultures today — and inside the academy — support bullying and demeaning actions (NAS et al, 2018).
One feature of kindness is that it enables both halves of the double meaning of the term “care.” To really care about someone or something, you need to tap into genuine kindness. To care for someone or something can merely be a job. But this job is also reduced without the impulse of kindness. That is why it is time to…
Put care back in your career.
“[B]y infusing bureaucratic maintenance work with an ethic of care, we can challenge contemporary workplace attitudes surrounding “productivity” and “efficiency,” moving toward the recognition of maintenance itself as a valued contribution. We can also broaden access to systems of information, thereby supporting its generative value…” (Maintainers et al, 2019).
The Maintainers <http://themaintainers.org/> extend an ethic of care to each other and to their work: they keep everything running, instead of inventing new stuff. This ethic is born in kindness, and requires a level of humility not casually found in the academy, where intellectual heroics overshadow moral choices.
Nel Noddings, who is a “care theorist,” someone who makes “the caring relation basic in moral theory” (2003), looks to recenter care as a normative behavior in education and the academy. She also separates the care that is expected in work (for example, doing something really well, or managing the needs of a student/patient) as a conformity to a workplace ethic, from caring: human acts “done out of love and natural inclination” (Noddings, 1988). What really works — in teaching and learning, and in team dynamics for collaborative research — is not completing the task of due-diligence, but rather building a framework of mutual caring nurtured from authentic kindness.
Bringing care into this discussion has now moved us away from communities, cohorts, and institutions. Care directs us back to intentions that are articulated in culture, but which also speak to being human in a mutually responsible human environment: a phrase not usually descriptive of the academy. “[W]e are led to redefine responsibility as response-ability, the ability to respond positively to others and not just to fulfill assigned duties” (ibid).
Open science is also science done through care and kindness: science that much more resembles the model of peer production within a commons, than it does a winner-take-all corporate struggle. “[W]ithout receiving conventional, tangible payments or favors in return, peers exercise kindness, benevolence, charity and generosity” (Benkler and Nissenbaum, 2006). Open science demands new levels of response-abilities: based on new and expanded academic freedoms (See: Values, freedoms and principles) and internet-enabled collaborations.
Coda: There are a lot more articles and books (such as Phillips and Taylor (2009): On Kindness) about the history of kindness and care that point out how these virtues were heralded as the basis of human happiness for centuries, and only recently (last 3–400 years) have these been eclipsed by more individualistic moral models (thanks to Hobbes, Kant, etc. — the usual suspects). So… practicing open science may also be good for your happiness. Doing open science can improve your happiness, and the happiness of those around you. How about that?
Benkler, Yochai, and Helen Nissenbaum. “Commons-Based Peer Production and Virtue.” Journal of Political Philosophy 14, no. 4 (2006): 394–419.
Clegg, S., and S. Rowland. “Kindness in pedagogical practice and academic life.” British Journal of Sociology of Education 31, no. 6 (2010): 719–735.
Juarrero, Alicia. Dynamics in Action. Cambridge, Ma: MIT Press, 1999.
National Academies of Sciences, Engineering, and Medicine, NAS Committee on the Impacts of Sexual Harassment in Academia, Committee on Women in Science, Engineering, and Medicine, and Policy and Global Affairs. Sexual Harassment of Women: Climate, Culture, and Consequences in Academic Sciences, Engineering, and Medicine. Edited by Paula A. Johnson, Sheila E. Widnall, and Frazier F. Benya. Washington, D.C.: National Academies Press, 2018. https://doi.org/10.17226/24994.
Noddings, Nel. “An Ethic of Caring and Its Implications for Instructional Arrangements.” American Journal of Education 96, no. 2 (1988): 215–230.
— — — . Happiness and Education. Cambridge University Press, 2003.
Phillips, Adam, and Barbara Taylor. On Kindness. 1st American ed. New York: Farrar, Straus and Giroux, 2009.
In a famous letter of 1813, Thomas Jefferson compared the spread of ideas to the way people light one candle from another:
“He who receives an idea from me, receives instruction himself without lessening mine; as he who lites his taper at mine, receives light without darkening me.”
Demand sharing means you can ask for everything you need to do your science… with one proviso…
PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.
We’ve all heard about the “sharing economy,” where we can gain new streams of income or convenience by simply sharing excess capacity (that spare room, the car ride, an electric scooter, etc.). And we’ve been told since childhood that sharing things we no longer need can help those with greater needs. Most of us feel we have a good idea about what it means to “share.” But then most of us are also mistaken, and here’s why.
Anthropologists who look at the ethnographies (and who do their own) of hunter-gatherer groups, and who sometimes also look at modern attempts to create sharing economies (e.g., Uber, Airbnb, etc.), tell us several things about sharing that most of us may find new and different from what we expected (Widlok, 2016; Suzman, 2018 <https://aeon.co/essays/why-inequality-bothers-people-more-than-poverty> Retrieved May 6, 2019). These ideas about sharing, synthesized from the study of human groups that have been successfully building their own lives for tens of thousands of years, say to us that we have “sharing” almost completely wrong.
• Real sharing is not charity. Charity is an artifact of the marketplace (and of personal wealth) and the logics of artificial scarcity.
• Sharing something you own that you are not using (like a spare room or space in your car) in exchange for cash is just another form of market transaction.
• Giving away things that you don’t need or no longer want is not a good example of sharing. This is an edge case.
Demand Sharing: share what is most important to you. Get what you need in return
In this handbook, we use the phrase “demand sharing” to designate a culturally advanced form of sharing, a type of cultural behavior that has been in widespread use of the majority of the human population for tens of thousands of years, and only recently eclipsed by marketplace logics in the past two to three hundred years. “Millions of years of evolution have designed us to live and think as community members. Within a mere two centuries we have become alienated individuals. Nothing testifies better to the awesome power of culture” (Harari, 2014).
Society uses demand sharing to fund its needs
A rather good (perhaps unexpected) example of demand sharing in modern society is having your representative government demand a tax that everybody pays, which then, for example, supports your state’s public colleges and universities (and pays your salary). That’s right; taxation is how a society demands of itself those resources it needs to prosper (Widlok, 2016).
Another example is sharing within a household, where family members can grab a snack from the refrigerator without much bother or need to justify or account for their choices. In the case of the academy, the “refrigerator” is the rapidly expanding corpus of digital research objects, and the family is fellow scientists who stock this with the outputs of their work, and who can then dive in and grab what they need for their own research. Note: this is a never-empty refrigerator, as these digital objects are not used up by their taking. Note again: they are anti-rivalrous: they gain value when they are shared. This is something every open scientist needs to remember.
Teaching and learning already require demand sharing. As an open scientist you’ve probably taught in a variety of classroom situations. Your students asked questions to extend their learning. Your best students (bless them) outright demanded to be taught. They marched into libraries (buildings, or on-line) and demanded the resources they need. They came to your office hours and demanded answers to their quandaries.
This means that nearly every scientist is well versed on how to participate in a demand-sharing economy. First, the state demands that its citizens fund the university, supporting teachers and learning. Then the student shows up and demands to be taught. We all did this as students. It’s not obscure, it’s how we learn.
Imagine a professor giving a lecture who stops in the middle and says, “This next part is really interesting; if you want to learn it, go to my app on your phone and deposit $10.” This should sound bizarre to you (if it doesn’t, then the neo-liberal university is your real home).
The for-profit textbook industry is very close to this same idea, particularly when a professor assigns their own textbook.
In part it sounds strange because the professor’s salary is already paid, hopefully through taxes. But mainly, it sounds wrong, as professors (who were once students) are completely happy for their students to learn. These learning moments in the classroom are seen as socially important and personally rewarding. When a student asks you a question, you do your best to help them learn something new.
In the hunter-gatherer culture, when a child comes to your fire and asks for some bit of meat from your catch, you always give it to them. Like food at a hunter-gatherer fire, information in a college is something that can be demanded. Demand sharing in education is a type of cultural economy where the norms and rules — the times and places, the manner of asking, the desire to teach and the value of learning — are well-known, without being written down. Students know they cannot demand the answers to a quiz in advance. What is sometimes forgotten is the need for and role of kindness in these interactions; more about that a bit later in the handbook (Deep Dive: Kindness).
Got a PhD? You know how to demand what you need
This means that you already know how to do demand sharing. Let’s look how demand sharing differs with what we just described as poor examples of “sharing.”
• You don’t give your classes as a form of charity (even though you may consider your own salary inadequate). You are a professional. Teaching is important. Your students have legitimate demands on your knowledge and your kindness. Passing on knowledge is why you teach.
• You also don’t teach your students content that you find worthless to you or loan them books that you are no longer satisfied with, unless these books are instructive in other ways. You share what is really important to your professional life: the best knowledge you’ve acquired.
• You expect students (at least, grad students) to demand from you what they need to learn and grow as scientists.
Demand sharing means sharing what is important to your research
This is the proviso we mentioned above. The same demand-sharing logic that collects the taxes that pay your salary, and enables your students to learn, also enables the academy to manage its knowledge resources for the benefit of all scientists, and the planet through the internet. Until today, a scientist might legitimately point out the huge amount of process-friction that would overly complicate sharing her data or workflows. A lot of the work of open-science advocates in the last two decades has been focused on reducing that friction through web-based platforms and services. Much of the remaining friction is cultural; linked back to institutional practices that do not reward or actively punish open resource sharing.
In an open-science, demand-sharing academic culture, sharing as much of your research as early as possible is a virtue strong enough to be a norm. Share what matters most to you: your methods, your findings (even null fundings), and your data. Share your best ideas openly, not simply those ideas that you have no interest in pursuing and every interest in having someone else pursue (See also: Idea Farming). Share your knowing by listening and adding to the conversation.
Open science requires generosity with a simple promise: each scientist will get more than they give. That’s the bargain the academy makes with you when you join and actively participate in the open-science academic society.
Of course the whole push to reboot the academy is based on the premise that this bargain has been bent and broken in many places.
This bargain is bolstered by the network effects of academy organizations. Demand sharing optimizes this bargain across academic networks and clubs (Redaction, 2016; Retrieved June 1, 2019).
Sharing imbeds your work into the community of science as a gift, a form of offering that also signals your membership. Sharing includes reviewing and acknowledging the work of your peers (See also: Perils of peer review). The open-science community creates its internal authority through relentless self-critique.
This authority works through a special soft of reciprocity and a level field of mutual status. As Polanyi (1962) noted, “[O]nce the novice has reached the grade of an independent scientist, there is no longer any superior above him. His submission to scientific opinion is entailed now in his joining a chain of mutual appreciations, within which he is called upon to bear his equal share of responsibility for the authority to which he submits.” This reciprocal authority of “mutual appreciations”, based on openly shared and critiqued knowledge is the basis for all applications of authority and leadership in an open-science academy (See also: Leadership and sharing).
The offerings you provide to the “republic of science” (Polanyi, ibid) lend you the cultural capital to demand the resources you need for your work from the abundance of open-access resources, and the knowing of others in your field. These, in turn, offer up their research for your use. As Hyde (2009) notes, the “constant and long-term exchanges between many people may have no ultimate ‘economic’ benefit, but through them society emerges where there was none before”; your contributions help create the academy society.
Amplified by the internet’s global reach, these exchanges expand and accelerate the process of science. You share the most important ideas you have, even at the risk of being scooped, because getting the most important work done now — whether you do this or someone else does (and attributes you with the idea) — moves science forward. You share your research results, all of them, knowing you will be critiqued by your peers, as you will also critique theirs.
“The self-image of humans who are embedded in sharing relations is not one of homo faber who creates his or her world out of nothing and without anyone else. Rather it is an image of what I have called homo sumens … who takes into use what is available through the company of others and that can be claimed from them” (Widlok, 2016).
Academic clubs: collectives for research collaboration
Demand sharing is a dense cultural practice, with its own behavioral expectations. When you share, you signal your desire to be included in the community. What you must learn, then, are the guidelines for demanding resources. “[T]he problem is not one of deciding what to give to whom but rather what to demand of whom. The onus is on the potential receiver to make his or her claim acceptable and the rules for appropriateness are not about acceptable giving but acceptable demanding” (Widlok, ibid; emphasis added).
The cultural shift to demand sharing will create a social basis for new science collectives, for “clubs” that share internally as though the club were a single, social organism. These formations are not entirely new. R&D Think-tanks have been funded for this purpose, and the NSF in the US spends a billion dollars a year funding academic workshops to assemble temporary collectives to solve common problems. “Club goods” are non-rivalrous inside the club, but not necessarily without shared costs (Hartley, et al, 2019). Thomas and Brown (2011) describe these as well, “Collectives are made up of people who generally share values and beliefs about the world and their place in it, who value participation over belonging, and who engage in a set of shared practices. Thus collectives are plural and multiple. They also both form and disappear regularly around different ideas, events, or moments.” Collectives enable collaboration across the internet, inform team-building, and open up the cultural situations for shared knowing.
The cultural practices of demand sharing will be emergent in the academy as open resources — including access to and discoverability of collaborators — become increasingly available in the next decade. This Handbook will help you to kickstart your own collectives, and forge demand-sharing cultural norms that suit your situation; see also Building new collectives.
Together with “fierce equality,” demand sharing as a cultural norm can help realize an actual sharing economy for the academy, separated from the arbitrary scarcity of the neo-liberal marketplace; a gift economy grounded in mutual appreciation and reciprocity. The particular practices of demand sharing will need to grow inside thousands of institutions across the globe. A goal of this Handbook is to give you the resources you need to build demand-sharing logics inside your academy homes. You can be a demand-sharing culture change agent by sharing your research objects and your research questions and problems; by listening more and adding your knowledge when asked. Demand answers from others; learn together. It’s science, not alchemy. You are not alone.
Harari, Y.N. Sapiens: A Brief History of Humankind. Random House, 2014.
Hartley, John, Jason Potts, Lucy Montgomery, Ellie Rennie, and Cameron Neylon. “Do We Need to Move from Communication Technology to User Community? A New Economic Model of the Journal as a Club.” Learned Publishing 32, no. 1 (January 2019): 27–35. https://doi.org/10.1002/leap.1228.
Hyde, Lewis. The Gift: Creativity and the Artist in the Modern World. Vintage, 2009.
Polanyi, M. “The Republic of Science: Its Political and Economic Theory.” Minerva 1 (1962): 54–73.
Thomas, Douglas, and John Seely Brown. A New Culture of Learning: Cultivating the Imagination for a World of Constant Change. Vol. 219. CreateSpace Lexington, KY, 2011.
Widlok, T. Anthropology and the Economy of Sharing. Routledge, 2016.
“‘Culture’ is everything we don’t have to do” (Brian Eno, 1996; W Magazine)
“‘Culture’ is anything you can get better at” Bruce Caron, 2019.
All the culture that fits: exploring the work of culture to prepare to change it
PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.
We want culture in the academy to work for us, instead of against us. The many meanings of the word “culture” — each with certain claims to capture essential aspects of this spectrum of human proclivity and activity — make the task of outlining a notion of the “work of culture” also a chore of definitions. What is it about culture that can be said to do work? And what work is important for open science?
One goal of this book is to help scholars who have little or no background in the academic study of culture to gain a sufficient purchase on this notion to become confident, productive agents of culture change for their home institutions, their professional associations and research organizations, and for the academy as a global science endeavor. Like quantum mechanics and machine intelligence, the serious study of culture is not one of these “dip your toes in the shallow end” kind of endeavor. However, with a roadmap through just enough of this contested space, even tenured chemistry professors (or pick your discipline) can become bonafide organizational culture-change agents.
Getting back to basics
Beginning anthropology classes might spend a month covering the “history of the anthropological ideas of culture.” These notions developed first through colonial excursions, and then with missionaries and colonial settlers, and finally ethnographers. Courses on “organizational culture” are now required in MBA curricula and iSchools.
Arjo Klamer (2017), a Dutch economist, introduces culture to his economics class by adding two meaning domains for this word: culture as the accomplishments of a society (e.g., baroque style as a form of European culture), and culture as creative activity within sectors of the economy (the arts, architecture, music, etc.). His first meaning gives us the adjective “cultured,” applied to individuals who exemplify a certain noticeable style; while his second is where you go to when you click on the “culture” link in an online magazine or newspaper.
Culture as a process
Folks who want to use culture and culture change as a resource or a tool to change social groups describe culture as a process. They then offer a method to intercept and guide this process (Marcus and Conner, 2014). Organizational management researchers are full of advice on the culture of organizations, but usually fail to look at how this type of culture fits into the larger sense of culture’s role in society or in individual identity. Anthropologists describe cultures and how these change without intervention, but little advice on how to intentionally change this. Here, you will find both anthropological and organizational perspectives, just so you are fully comfortable that you’ve travelled the entire landscape of the term “culture.”
Do you own your culture, or does your culture own you?
“Culture is public because meaning is” (Geertz, 1973).
Much of the disputed territory for culture, whether as an object of study, or as a field for intentional change, is centered on how culture is carried more or less unconsciously by the individual. Sometimes it feels as though we’ve been “marinated” in cultural practices our entire lives: language, cuisine, music, art, and now online content. There is a part of culture that is tacit, embodied, unspoken, and non-conscious. Culture theories tell us this, and they are not wrong. This aspect of culture is often used to demonstrate how difficult it is to manage culture.
A vague, squishy word, indeed
Jean-Louis Gassée (not an anthropologist; but rather of Apple, BeOS, and Palm fame), in a blog about Intel’s “toxic culture” writes:
“Our powerful human emotions are bundled into something we call Culture, itself a vague, squishy word……Culture develops within us in a manner similar to our taste buds: Our gustatory education starts with Mother’s milk and accumulates over time. The trouble with our acquired tastes, particularly in the realm of ideas, is that they drop below our consciousness: Raw data are filtered, judged, and labeled before being passed to our conscious, ‘rational’ processes.”
Gassée is pointing out that parts of the repertoire of shared meanings, behaviors, and sentiments that people would label “cultural” are known without any explicit knowledge of how and when we came to know these; and even less ability to describe them.
Schein (2010) calls this a cultural “layer.” This layer is learned from birth at home, and then in school, and then in the workplace, where the same tacit layer proves the hardest part to change. When your company/university/agency is running on a tacit culture layer, instead of on a reflexive intentional culture layer, it is most vulnerable to becoming toxic (Deep Dive: Toxic Culture).
Science is a reflexive, interrogative activity
Fortunately, the main aspects of academy culture we are hoping to change can all be made explicit and available to reflexive rebooting. In fact, open science is not reinventing science as much as clearing away the extraneous cultural underbrush (such as journal impact factors) that has collected in the past half-century or so. Scientists can openly interrogate these practices, and collectively move away from perverse incentives, conflicts of interest, and culturally-supported bad behavior in the academy. The leading advice to Silican Valley CEOs today is to avoid “f*cking up your culture” (See also: Don’t F*ck Up Your Culture; Retrieved May 17, 2019). The academy might want to listen here.
You cannot really avoid culture if you want change
A good point is worth saying twice: you may be an open-science pioneer who is eager and intent to bring productive changes to the academy, and yet still be uncomfortable with the notion of culture. You might prefer to offer solutions (e.g., coercive rules enforced by governments and funding organizations, novel technology platforms, and manifestos — so many manifestos) that, you hope, would shape “social behavior” without needing to confront or even consider culture. You look at the term “culture” and see a morass of competing meanings, with tangled and complex implications for the use of the term. How do you defend a program to change culture when you can’t get any three people in a room to agree on what culture means?
Scientists are many things. Each of these things have something in common: a desire for precision. The “vague, squishy” term “culture” offers very little precision and a whole load of ambiguity and complexity. As a scientist, you already have your hands full of ambiguity and complexity; you are striving to understand the inherent, emergent complexity of the universe. You rely on instruments that achieve ever-better accuracy and precision to help you extract some level of near-certainty to observe your object of study.
Many scientists are dismayed by the sheer amount of fuzziness surrounding the notion of culture. So the project at hand is to un-fuzzy that corner of culture where the academy can work on intentional changes to promote open science. The rest can remain terra incognito. The fact is, you don’t need to be an anthropologist to put culture to work in your organization.
In short: the good news is that the cultural work of open science is centered on those aspects of culture that can be intentionally described, discussed, and refactored — even if some of these might later become routine and get framed as default expectations. It’s not a bad thing to have your active culture also inform the tacit level of culture, it’s actually a goal: norms are cultural behaviors and attitudes that have become tacit culture. A norm is when “we open scientists do things like this,” and think: why would we do anything else?
Culture: trimmed down to size for the open scientist
Here we will trim the semantic tangle of the term “culture” to a more specific notion of culture: to the point where it can serve our understanding of how this works and how this fits into the future of the academy. The word “culture” will still hold all of its diverse and multiplex meanings everywhere else, however, here we’ll just agree to use it in one specific way to cut through a lot of the semantic shrubbery it has acquired over the centuries and around the globe.
Learning from anthropology
We can start by looking at some general attributes of “culture.” In his 1993 book, Culture, Chris Jenks notes (following Ralph Parsons):
“…for present purposes three prominent keynotes of the discussion [around culture] may be picked out: first, that culture is transmitted, it constitutes a heritage or a social tradition; secondly, that it is learned, it is not a manifestation, in particular content, of man’s genetic constitution; and third, that it is shared. Culture, that is, is on the one hand the product of, on the other hand a determinant of, systems of human social interaction” (Jenks 1993: 59).
Lets put these verbs into the following order: learn (first exposure) → share (locally) → transmit (across space/time). Repeat as needed. This sounds a lot like education, something the academy already does. For the individual, this process is, or can be, a lifelong activity. What Clifford Geertz reminds us is that these cultural activities are public. Nothing is cultural until it is shared. That means these activities are available to study, and to change, and to be changed through intentional intervention (although somewhat less available when they are only tacit).
One easy way to see what Jenks is proposing here is to substitute “language” for “culture;” after all, language is a good part of any society’s cultural repertoire. Saying that language is transmitted is to acknowledge that we don’t need to invent our own language anew every generation. Saying language is learned explains that we acquire this through learning as children and then hone this learning throughout our lives. To say that language is shared points to a key concept: we need others to make this work; it’s called “conversation”. In many ways, language is primarily a type of sharing. Other skills and cultural content exhibit these same features.
The reverse is also true. If a language is not transmitted over time it “dies”. If a person doesn’t learn a language, they are left outside the conversations that happen in that language. And when a language ceases to be shared in everyday life (e.g., it becomes a “sacred” language that can only be spoken in certain places/times), other language forms will take over in daily life. Languages change all the time. Remember that. They manifest lifelong, tacit cultural practices, and they still change.
Culture comes in community boxes
“Community, therefore, is where one learns and continues to practice how to ‘be social’. At the risk of substituting one indefinable category for another, we could say it is where one acquires ‘culture’” (Cohen, 1985).
The usual container for a culture is called “community.” As an organization grows and governs its own cultural work, you can say that the group becomes a community. You can dive into “community” elsewhere in the Handbook (Deep Dive: Communities, Collectives, and Commons). Notions of community will also be threaded into many of the Handbook chapters.
Meaning, Symbols, and Memes; oh my!
Exactly what is learned, transmitted, and shared as culture is complicated. “Meaning” usually pops up here, together with “symbols” (meaning carriers). In many ways anything that can be learned (anything you can get better at by learning this), and that must be shared in order to make sense as something to do (write a song, choose a new fashion statement, enter a conversation, sports, theatre, etc.) becomes culture when the various meanings of that learned behavior are also shared. You cannot have your own private culture. That said, you can have a very small community with its own distinguished cultural behaviors.
Memes are symbols that have been reimagined as cultural-genetic replicators. The analogy to biology is intentional, and meme theorists also talk of culture change as evolution. Since the 1970s, meme theories have been proposed to explain how certain cultural content packages spread and persist.
“[Richard] Dawkin’s way of speaking was not meant to suggest that memes are conscious actors, only that they are entities with interests that can be furthered by natural selection. Their interests are not our interests. ‘A meme,’ [Daniel] Dennett says, ‘is an information packet with attitude’” (Gleick, 2011).
The notion of a meme is centered on the idea that humans as social beings are shaped by culture the same way their bodies are shaped by their DNA. If you want to explore memes a bit more, here’s a good introduction (by Dennett) and some good counter arguments (by Lanier). Here we will talk about meaning and symbols and culture change, but you are certainly free to talk about memes and evolution. You can also look into “cultural science,” where evolutionary cultural studies are being done.
Culture is a plural noun
Not grammatically, of course, but we have seen and continue to see around us how cultural notions, skills, and activities are typically multiple, contested, fragile, and liable to change. Individuals tend to privilege those notions, skills, and activities they have invested time to learn (so nobody wants to be forced to use a different language). However, since culture must be shared to be viable, individuals continually find themselves in conversation with others who have differing cultural inventories. Culture is like a life-long song we only sing once, and none of us has been handed the score for the next chorus. We just keep on singing, in multipart harmony.
Knowing is the intrinsic work of culture in your organization
Of course, culture is not only a noun. Humans are cultural beings. Humans have culture. Humans do culture. There is a lot of culture going on all the time. More recent takes on organizational culture reject this as being just some packet of ideas that gets passed around.Today, more than ever before, culture is viral, active, flowing (Appadurai, 1996). Today, culture is on the internet too.
The recent work of John Seely Brown, coming out of organizational knowledge theories in the mid 1990s (See: Boland Jr. and Tankasi, 1995), has added (or recovered) a cultural angle on knowledge management (Cook and Brown, 1999). Instead of organizations stewarding an inventory of knowledge objects, what they need to do is open up contexts and spaces for knowing: contexts for the transmitting, learning, and sharing between and among their participants (Thomas and Brown, 2011).
This concept was then picked up by David Snowden and others (Kurtz and Snowden, 2003), who mapped the contexts of knowing and “sense-making” into what they called the Cynefin Framework (https://en.wikipedia.org/wiki/Cynefin_framework, Retrieved May 20, 2019). This framework is largely about identifying types of knowing — and ways of deciding — in corporations, as a corrective to the prior knowledge management systems which only covered tacit and discursive knowledge objects (Wenger et al, 2002).
“The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these — simple, complicated, complex, and chaotic — require leaders to diagnose situations and to act in contextually appropriate ways. The fifth — disorder — applies when it is unclear which of the other four contexts is predominant” (Snowden and Boone, 2007).
The Cynefin Framework describes several domains of knowing; the core qualities of knowing are different in each of these. Knowing is an activity, an action, not a commodity, not a thing to be managed.
Knowing, or sense-making, is an intrinsic work for organizational culture. This is particularly true in the academy, where new knowledge and learning outcomes are a chief value proposition. Scientific “knowledge” is an output of shared knowing.
The challenge is that these domains are not always fully manageable, and neither are the humans that engage in knowing with each other, most particularly in the complex domain of the infinite game. Knowing is why we might learn more in a 10 minute conversation than we can from a 1000 page book. Knowing is how scientists play the infinite game with one another. You can briefly explore the infinite game by going back to the Things about science section.
Cynefin for the Academy
For now, the main take-aways from using the Cynefin Framework for the academy are the following:
First: it helps to explain the difference between doing science, talking/writing science, and telling others about science. These occur in different domains; and,
Second: it begins to describe the complex, emergent space of the infinite game. Learning this is central to building academy governance for game play. For centuries, most scientists, or earlier, natural philosophers, and before them, philosophers, played the infinite game individually. Today, science and learning is a team sport, and the academy needs to find ways to govern team play (Deep Dive: Knowing to Play the Infinite Game).
The Cynefin Framework is explored at length in Deep Dive sections on Leadership and Learning, so we will not pursue it further here, except for this: The Handbook also presents a version of the Cynefin Framework that uses three modal types of cultural activity to represent the framework’s logics (complex, complicated, simple). These modes are: festival, game, and spectacle. You will need to ask this question a lot: upon which logic does your organization base its decisions? Starting with the wrong logic will lead to bad, sometimes very bad, decisions. A lot of toxic culture in the academy is based on decisions arrived at in the wrong domain.
Festival: For those who grew up in the parts of the planet (such as most of North America) without festivals that involve actual danger, nudity, running with fire, social exposure, complex body skills, radical comedy — the various ingredients of festivity that make these events complex, emergent activities — we are not talking about the annual petunia festival here. Also note that the best intellectual conversations are like running with fire.
The Cultural Work of Social Organizations
Cultural practices and social organizations are intertwined in time and space. Social organizations are the social “appliances,” the furniture, that anchor human groups into more durable cultural contexts, which they support and are, in turn, supported by. These contexts expand our capacity for collective action, including economic and political action. Just as we do not need to—or get to—invent our own language, we don’t get to invent most of the social groups we intersect in our lives. But we can change them.
In order to pursue the intrinsic cultural work of the academy, we build communities inside organizations that use governance processes to support sharing knowing. We use can our organizations to manage other, social and economic tasks. If knowing is a dance, then community is the dance floor, and the organization is the dance hall.
In the twenty-five years since Jenks’ book, culture has seen a lot of new attention. From the portmanteau academic discipline of “cultural studies” to the cubicles of Silicon Valley start-up companies, the importance of culture for the everyday life and future prospects of societies and corporations has become a central theme. It’s high time for the academy to take a culture turn. You can help.
Now you know enough about the various aspects of culture to start rolling up your pants and wading in. You know that culture is (and must be) learned, shared, and transmitted. Most of culture is really vulnerable to intervention or substitution. Culture describes a broad range of human activities and a layer of meaning that is spread over (or under) social activities and organizations.
Knowing is an intrinsic work of culture, a primary activity for all cultural activities, but particularly for those, like science, that are involved in the infinite game. Knowing happens in more than one domain. The meanings of culture are all public. You can find them, interrogate them, and, yes, change them. That’s the next topic in the Handbook: The task: culture change.
Appadurai, Arjun. Modernity Al Large: Cultural Dimensions of Globalization. Vol. 1. U of Minnesota Press, 1996.
Boland Jr, Richard J, and Ramkrishnan V Tenkasi. “Perspective Making and Perspective Taking in Communities of Knowing.” Organization Science 6, no. 4 (1995): 350–372.
Cohen, A.P. The Symbolic Construction of Community. Chichester, Sussex: Ellis Horwood Ltd, 1985.
Cook, D.N., and John Seely Brown. “Bridging Epistemologies: The Generative Dance between Organizational Knowledge and Organizational Knowing.” Organization Science 10, no. 4 (1999): 381–400. http://www.jstor.org/stable/2640362.
Geertz, C. The Interpretation of Cultures. New York: Basic Books, 1973.
Gleick, James. The Information: A History, a Theory, a Flood. 1st ed. New York: Pantheon Books, 2011.
Jenks, Chris. Culture. London And. New York: Routledge, 1993.
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!
This talk was generously supported by the Alfred P. Sloan Foundation