Digifesto

bodies and liberal publics in the 20th century and today

I finally figured something out, philosophically, that has escaped me for a long time. I feel a little ashamed that it’s taken me so long to get there, since it’s something I’ve been told in one way or another many times before.

Here is the set up: liberalism is justified by universal equivalence between people. This is based in the Enlightenment idea that all people have something in common that makes them part of the same moral order. Recognizing this commonality is an accomplishment of reason and education. Whether this shows up in Habermasian discourse ethics, according to which people may not reason about politics from their personal individual situation, or in the Rawlsian ‘veil of ignorance’, in which moral precepts are intuitively defended under the presumption that one does not know who or where one will be, liberal ideals always require that people leave something out, something that is particular to them. What gets left out is people’s bodies–meaning both their physical characteristics and more broadly their place in lived history. Liberalism was in many ways a challenge to a moral order explicitly based on the body, one that took ancestry and heredity very seriously. So much a part of aristocratic regime was about birthright and, literally, “good breeding”. The bourgeois class, relatively self-made, used liberalism to level the moral playing field with the aristocrats.

The Enlightenment was followed by a period of severe theological and scientific racism that was obsessed with establishing differences between people based on their bodies. Institutions that were internally based on liberalism could then subjugate others, by creating an Other that was outside the moral order. Equivalently, sexism too.
Social Darwinism was a threat to liberalism because it threatened to bring back a much older notion of aristocracy. In WWII, the Nazis rallied behind such an ideology and were defeated in the West by a liberal alliance, which then established the liberal international order.

I’ve got to leave out the Cold War and Communism here for a minute, sorry.

Late modern challenges to the liberal ethos gained prominence in activist circles and the American academy during and following the Civil Rights Movement. These were and continue to be challenges because they were trying to bring bodies back into the conversation. The problem is that a rules-based order that is premised on the erasure of differences in bodies is going to be unable to deal with the political tensions that precisely do come from those bodily differences. Because the moral order of the rules was blind to those differences, the rules did not govern them. For many people, that’s an inadequate circumstance.

So here’s where things get murky for me. In recent years, you have had a tension between the liberal center and the progressive left. The progressive left reasserts the political importance of the body (“Black Lives Matter”), and assertions of liberal commonality (“All Lives Matter”) are first “pushed” to the right, but then bump into white supremacy, which is also a reassertion of the political importance of the body, on the far right. It’s worth mention Piketty, here, I think, because to some extent that also exposed how under liberal regimes the body has secretly been the organizing principle of wealth through the inheritance of private property.

So what has been undone is the sense, necessary for liberalism, that there is something that everybody has in common which is the basis for moral order. Now everybody is talking about their bodily differences.

That is on the one hand good because people do have bodily differences and those differences are definitely important. But it is bad because if everybody is questioning the moral order it’s hard to say that there really is one. We have today, I submit, a political nihilism crisis due to our inability to philosophically imagine a moral order that accounts for bodily difference.

This is about the Internet too!

Under liberalism, you had an idea that a public was a place people could come to agree on the rules. Some people thought that the Internet would become a gigantic public where everybody could get together and discuss the rules. Instead what happened was that the Internet became a place where everybody could discuss each other’s bodies. People with similar bodies could form counterpublics and realize their shared interests as body-classes. (This piece by David Weinberger critiquing the idea of an ‘echo chamber’ is inspiring.) Within these body-based counterpublics each form their own internal moral order whose purpose is to mobilize their body-interests against other kinds of bodies. I’m talking about both black lives matter and white supremacists here, radical feminists and MRA’s. They are all buffeting liberalism with their body interests.

I can’t say whether this is “good” or “bad” because the moral order is in flux. There is apparently no such thing as neutrality in a world of pervasive body agonism. That may be its finest criticism: body agonism is politically unstable. Body agonism leads to body anarchy.

I’ll conclude with two points. The first is that the Enlightenment view of people having something in common (their personhood, their rationality, etc.) which put them in the same moral order was an intellectual and institutional accomplishment. People do not naturally get outside themselves and put themselves in other people’s shoes; they have to be educated to do it. Perhaps there is a kernal of truth here about what moral education is that transcends liberal education. We have to ask whether today’s body agonism is an enlightened state relative to moral liberalism because it acknowledges a previously hidden descriptive reality of body difference and is no longer so naive, or if body agonism is a kind of ethical regress because it undoes moral education, reducing us to a more selfish state of nature, of body conflict, albeit in a world full of institutions based on something else entirely.

The second point is that there is an alternative to liberal order which appears to be alive and well in many places. This is an order that is not based on individual attitudes for legitimacy, but rather is more about the endurance of institutions for their own sake. I’m referring of course to authoritarianism. Without the pretense of individual equality, authoritarian regimes can focus on maintaining power on their own terms. Authoritarian regimes do not need to govern through moral order. U.S. foreign policy used to be based on the idea that such amoral governance would be shunned. But if body agonism has replaced the U.S. international moral order, we no longer have an ideology to export or enforce abroad.

Notes on Omi and Winant, 2014, “Ethnicity”

I’m continuing to read Omi and Winant’s Racial Formation in the United States (2014). These are my notes on Chapter 1, “Ethnicity”.

There’s a long period during which the primary theory of race in the United States is a theological and/or “scientific” racism that maintains that different races are biologically different subspecies of humanity because some of them are the cursed descendants of some tribe mentioned in the Old Testament somewhere. In the 1800’s, there was a lot of pseudoscience involving skull measurements trying to back up a biblical literalism that rationalized, e.g., slavery. It was terrible.

Darwinism and improved statistical methods started changing all that, though these theological/”scientific” ideas about race were prominent in the United States until World War II. What took them out of the mainstream was the fact that the Nazis used biological racism to rationalize their evilness, and the U.S. fought them in a war. Jewish intellectuals in the United States in particular (and by now there were a lot of them) forcefully advocated for a different understanding of race based on ethnicity. This theory was dominant as a replacement for theories of scientific racism between WWII and the mid-60’s, when it lost its proponents on the left and morphed into a conservative ideology.

To understand why this happened, it’s important to point out how demographics were changing in the U.S. in the 20th century. The dominant group in the United States in the 1800’s were White Anglo-Saxon Protestants, or WASPs. Around 1870-1920, the U.S. started to get a lot more immigrants from Southern and Eastern Europe, as well as Ireland. These often economic refugees, though there were also people escaping religious persecution (Jews). Generally speaking these immigrants were not super welcome in the United States, but they came in at what may be thought of as a good time, as there was a lot of economic growth and opportunity for upward mobility in the coming century.

Partly because of this new wave of immigration, there was a lot of interest in different ethnic groups and whether or not they would assimilate in with the mainstream Anglo culture. American pragmatism, of the William James and Jown Dewey type, was an influential philosophical position in this whole scene. The early ethnicity theorists, who were part of the Chicago school of sociology that was pioneering grounded, qualitative sociological methods, were all pragmatists. Robert Park is a big figure here. All these guys apparently ripped off W.E.B. Du Bois, who was trained by William James and didn’t get enough credit because he was black.

Based on the observation of these European immigrants, the ethnicity theorists came to the conclusion that if you lower the structural barriers to participation in the economy, “ethnics” will assimilate to the mainstream culture (melt into the “melting pot”) and everything is fine. You can even tolerate some minor ethnic differences, resulting in the Italian-Americans, the Irish-Americans, and… the African-American. But that was a bigger leap for people.

What happened, as I’ve mentioned, is that scientific racism was discredited in the U.S. partly because it had to fight the Nazis and had so many Jewish intellectuals, who had been on the wrong end of scientific racism in Europe and who in the U.S. were eager to become “ethnics”. These became, in essence, the first “racial liberals”. At the time there was also a lot of displacement of African Americans who were migrating around the U.S. in search of economic opportunities. So in the post-war period ethnicity theorists optimistically proposed that race problems could be solved by treating all minority groups as if they were Southern and Eastern European immigrant groups. Reduce enough barriers and they would assimilate and/or exist in a comfortable equitable pluralism, they thought.

The radicalism of the Civil Rights movement broke the spell here, as racial minorities began to demand not just the kinds of liberties that European ethnics had taken advantage of, but also other changes to institutional racism and corrections to other racial injustices. The injustices persisted in part because racial differences are embodied differently than ethnic differences. This is an academic way of saying that the fact that (for example) black people often look different from white people matters for how society treats them. So treating race as a matter of voluntary cultural affiliation misses the point.

So ethnicity theory, which had been critical for dismantling scientific racism and opening the door for new policies on race, was ultimately rejected by the left. It was picked up by neoconservatives through their policies of “colorblindness”, which Omi and Winant describe in detail in the latter parts of their book.

There is a lot more detail in the chapter, which I found quite enlightening.

My main takeaways:

  • In today’s pitched media battles between “Enlightenment classical liberalism” and “postmodern identity politics”, we totally forget that a lot of American policy is based on American pragmatism, which is definitely neither an Enlightenment position nor postmodern. Everybody should shut up and read The Metaphysical Club.
  • There has been a social center, with views that are seen as center-left or center-right depending on the political winds, since WWII. The adoption of ethnicity theory into the center was a significant culture accomplishment with a specific history, however ultimately disappointing its legacy has been for anti-racist activists. Any resurgence of scientific racism is a definite backslide.
  • Omi and Winant are convincing about the limits of ethnicity theory in terms of: its dependence on economic “engines of mobility” that allow minorities to take part in economic growth, its failure to recognize the corporeal and ocular aspects of race, and its assumption that assimilation is going to be as appealing to minorities as it is to the white majority.
  • Their arguments about colorblind racism, which are at the end of their book, are going to be doing a lot of work and the value of the new edition of their book, for me at least, really depends on the strength of that theory.

Notes on Racial Formation by Omi and Winant, 2014, Introduction

Beginning to read Omi and Winant, Racial Formation in the United States, Third Edition, 2014. These are notes on the introduction, which outlines the trajectory of their book. This introduction is available on Google Books.

Omi and Winant are sociologists of race and their aim is to provide a coherent theory of race and racism, particularly as a United States phenomenon, and then to tell a history of race in the United States. One of their contentions is that race is a social construct and therefore varies over time. This means, in principle, that racial categories are actionable, and much of their analysis is about how anti-racist and racial reaction movements have transformed the politics and construction of race over the course of U.S. history. On the other hand, much of their work points to the persistence of racial categories despite the categorical changes.

Since the Third Edition, in 2014, comes twenty years after the Second Edition, much of the new material in the book addresses specifically what they call colorblind racial hegemony. This is a response to the commentary and question around the significance of Barack Obama’s presidency for race in America. It is interesting reading this in 2018, as in just a few brief years it seems like things have changed significantly. It’s a nice test, then to ask to what extent their theory explains what happened next.

Here is, broadly speaking, what is going on in their book based on the introduction.

First, they discuss prior theories of race they can find in earlier scholarship. They acknowledge that these are interesting lenses but believe they are ultimately reductionist. They will advance their own theory of racial formation in contrast with these. In the background of this section but dismissed outright is the “scientific” racism and religious theories of race that were prevalent before World War II and were used to legitimize what Omi and Winant call racial domination (this has specific meaning for them). Alternative theories of race that Omi and Winant appear to see as constructive contributions to racial theory include:

  • Race as ethnicity. As an alternative to scientific racism, post WWII thinkers advanced the idea of racial categories as reducing to ethnic categories, which were more granular social units based on shared and to some extent voluntary culture. This conception of race could be used for conflicting political agendas, including both pluralism and assimilation.
  • Race as class. The theory attempted to us economic theories–including both Marxist and market-based analysis–to explain race. Omi and Winant think this–especially the Marxist theory–was a productive lens but ultimate a reductive one. Race cannot be subsumed to class.
  • Race as nationality. Race has been used as the basis for national projects, and is tied up with the idea of “peoplehood”. In colonial projects especially, race and nationality are used both to motivate subjugation of a foreign people, and is also used in resistance movements to resist conquest.

It is interesting that these theories of race are ambiguous in their political import. Omi and Winant do a good job of showing how multi-dimensional race really is. Ultimately they reject all these theories and propose their own, racial formation theory. I have not read their chapter on it yet, so all I know is that: (a) they don’t shy away from the elephant in the room, which is that there is a distinctively ‘ocular’ component to race–people look different from each other in ways that are hereditary and have been used for political purposes, (b) they maintain that despite this biological aspect of race, the social phenomenon of race is a social construct and primarily one of political projects and interpretations, and (c) race is formed by a combination of action both at the representational level (depicting people in one way or another) and at the institutional level, with the latter determining real resource allocation and the former providing a rationalization for it.

Complete grokking of the racial formation picture is difficult, perhaps. This may be why instead of having a mainstream understanding of racial formation theory, we get reductive and ideological concepts of race active in politics. The latter part of Omi and Winant’s book is their historical account of the “trajectory” of racial politics in the United States, which they see in terms of a pendulum between anti-racist action (with feminist, etc., allies) and “racial reaction”–right-wing movements that subvert the ideas used by the anti-racists and spin them around into a backlash.

Omi and Winant describe three stages of racial politics in United States history:

  • Racial domination. Slavery and Jim Crow before WWII, based on religious and (now discredited, pseudo-)scientific theories of racial difference.
  • Racial hegemony. (Nod to Gramsci) Post-WWII race relations as theories of race-as-ethnicity open up egalitarian ideals. Opens way for Civil Rights movement.
  • Colorblind racism. A phase where the official ideology denies the significance of race in society while institutions continue to reinforce racial differences in a pernicious way. Necessarily tied up with neoliberalism, in Omi and Winant’s view.

The question of why colorblind racism is a form of racism is a subtle one. Omi and Winant do address this question head on, and I am in particular looking forward to their articulation of the point. Their analysis was done during the Obama presidency, which did seem to move the needle on race in a way that we are still seeing the repercussions of today. I’m interested in comparing their analysis with that of Fraser and Gilman. There seem to be some productive alignments and tensions there.

Notes on Pasquale, “Tech Platforms and the Knowledge Problem”, 2018

I’ve taken a close look at Frank Pasquale’s recent article, “Tech Platforms and the Knowledge Problem” in American Affairs. This is a topic that Pasquale has had his finger on the pulse of for a long time, and I think with this recent articulation he’s really on to something. It’s an area that’s a bit of an attractor state in tech policy thinking at the moment, and as I appear to be in that mix more than ever before, I wanted to take a minute to parse out Frank’s view of the state of the art.

Here’s the setup: In 1945, Hayek points out that the economy needs to be managed somehow, and that this is the main economic use of information/knowledge. Hayek sees the knowledge as distributed and coordination accomplished through the price mechanism. Today we have giant centralizing organizations like Google and Amazon mediating markets, and it’s possible that these have the kind of ‘central planning’ role that Hayek didn’t want. There is a status quo where these companies run things in an unregulated way. Pasquale, being a bit of a regulatory hawk, not unreasonably thinks this may be disappointing and traces out two different modes of regulatory action that could respond to the alleged tech company dominance.

He does this with a nice binary opposition between Jeffersonians, who want to break up the big companies into smaller ones, and Hamiltonians, who want to keep the companies big but regulate them as utilities. His choice of Proper Nouns is a little odd to me, since many of his Hamiltonians are socialists and that doesn’t sound very Hamiltonian to me, but whatever: what can you do, writing for Americans? This table sums up some of the contrasts. Where I’m introducing new components I’m putting in a question mark (?).

Jeffersonian Hamiltonian
Classical competition Schumpeterian competition
Open Markets Institute, Lina Khan Big is Beautiful, Rob Atkinson, Evgeny Morozov
Fully automated luxury communism
Regulatory capture (?) Natural monopoly
Block mergers: unfair bargaining power Encourage mergers: better service quality
Allow data flows to third parties to reduce market barriers Security feudalism to prevent runaway data
Regulate to increase market barriers
Absentee ownership reduces corporate responsibility Many small companies, each unaccountable with little to lose, reduces corporate responsibility
Bargaining power of massive firms a problem Lobbying power of massive firms a problem (?)
Exit Voice
Monopoly reduces consumer choice Centralized paternalistic AI is better than consumer choice
Monopoly abuses fixed by competition Monopoly abuses fixed by regulation
Distrust complex, obscure corporate accountability Distrust small companies and entrepreneurs
Platforms lower quality; killing competition Platforms improve quality via data size, AI advances; economies of scale
Antitrust law Public utility law
FTC Federal Search Commission?
Libertarianism Technocracy
Capitalism Socialism
Smallholding entrepreneur is hero Responsible regulator/executive is hero

There is a lot going on here, but I think the article does a good job of developing two sides of a dialectic about tech companies and their regulation that’s been emerging. These framings extend beyond the context of the article. A lot of blockchain proponents are Jeffersonian, and their opponents are Hamiltonian, in this schema.

I don’t have much to add at this point except for the observation that it’s very hard to judge the “natural” amount of industrial concentration in these areas in part because of the crudeness of the way we measure concentration. We easily pay attention to the top five or ten companies in a sector. But we do so by ignoring the hundred or thousand or more very small companies. It’s just incorrect to say that there is only one search engine or social network; it’s just that the size distribution for the many many search engines and social networks is very skewed, like a heavy tail or log normal distribution. There may be perfectly neutral, “complex systems” oriented explanations for this distribution that make it very robust even with a number of possible interventions.

If that’s true, there will always be many many small companies and a few market leaders in the tech sector. The small companies will benefit from Jeffersonian policies, and those invested in the market leaders will benefit (in some sense) from Hamiltonian policies. The question of which strategy to take then becomes a political matter: it depends on the self-interest of differently positioned people in the socio-economic matrix. Or, alternatively, there is no tension between pursuing both kinds of policy agenda, because they target different groups that will persist no matter hat regime is in place.

population traits, culture traits, and racial projects: a methods challenge #ica18

In a recent paper I’ve been working on with Mark Hannah that he’s presenting this week at the International Communications Association conference, we take on the question of whether and how “big data” can be used to study the culture of a population.

By “big data” we meant, roughly large social media data sets. The pitfalls of using this sort of data for any general study of a population are perhaps best articled by Tufekci (2014). In short: studies based on social media data are often sampling on the dependent variable because they only consider the people representing themselves on social media, though this is only a small portion of the population. To put it another way, the sample suffers from the 1% rule of Internet cultures: for any on-line community, only 1% create content, 10% interact with the content somehow, and the rest lurk. The behavior and attitudes of the lurkers, in addition to any field effects in the “background” of the data (latent variables in the social field of production), are all out of band and so opaque to the analyst.

By “the culture of a population”, we meant something specific: the distribution of values, beliefs, dispositions, and tastes of a particular group of people. The best source we found on this was Marsden and Swingle (1994), and article from a time before the Internet had started to transform academia. Then and perhaps now, the best way to study the distribution of culture across a broad population was a survey. The idea is that you sample the population according to some responsible statistics, you ask them some questions about their values, beliefs, dispositions, and tastes, and you report the results. Viola!

(Given the methodological divergence here, the fact that many people, especially ‘people on the Internet’, now view culture mainly through the lens of other people on the Internet is obviously a huge problem. Most people are not in this sample, and yet we pretend that it is representative because it’s easily available for analysis. Hence, our concept of culture (or cultures) is screwy, reflecting much more than is warranted whatever sorts of cultures are flourishing in a pseudonymous, bot-ridden, commercial attention economy.)

Can we productively combine social media data with surveys methods to get a better method for studying the culture of a population? We think so. We propose the following as a general method framework:

(1) Figure out the population of interest by their stable, independent ‘population traits’ and look for their activity on social media. Sample from this.

(2) Do exploratory data analysis to inductively get content themes and observations about social structure from this data.

(3) Use the inductively generated themes from step (2) to design a survey addressing cultural traits of the population (beliefs, values, dispositions, tastes).

(4) Conduct a stratified sample specifically across social media creators, synthesizers (e.g. people who like, retweet, and respond), and the general population and/or known audience, and distribute the survey.

(5) Extrapolate the results to general conclusions.

(6) Validate the conclusions with other data or not discrepancies for future iterations.

I feel pretty good about this framework as a step forward, except that in the interest of time we had to sidestep what is maybe the most interesting question raised by it, which is: what’s the difference between a population trait and a cultural trait.

Here’s what we were thinking:

Population trait Cultural trait
Location Twitter use (creator, synthesizer, lurker, none)
Age Political views: left, right, center
Permanent unique identifier Attitude towards media
Preferred news source
Pepsi or coke?

One thing to note: we decided that traits about media production and consumption were a subtype of cultural traits. I.e., if you use Twitter, that’s a particular cultural trait that may be correlated with other cultural traits. That makes the problem of sampling on the dependent variable explicit.

But the other thing to note is that there are certain categories that we did not put on this list. Which ones? Gender, race, etc. Why not? Because choosing whether these are population traits or cultural traits opens a big bag of worms that is the subject of active political contest. That discussion was well beyond the scope of the paper!

The dicey thing about this kind of research is that we explicitly designed it to try to avoid investigator bias. That includes the bias of seeing the world through social categories that we might otherwise naturalize of reify. Naturally, though, if we were to actually conduct this method on a sample, such as, I dunno, a sample of Twitter-using academics, we would very quickly discover that certain social categories (men, women, person of color, etc.) were themes people talked about and so would be included as survey items under cultural traits.

That is not terrible. It’s probably safer to do that than to treat them like immutable, independent properties of a person. It does seem to leave something out though. For example, say one were to identify race as a cultural trait and then ask people to identify with a race. Then one takes the results, does a factor analysis, and discovers a factor that combines a racial affinity with media preferences and participation rates. It then identifies the prevalence of this factor in a certain region with a certain age demographic. One might object to this result as a representation of a racial category as entailing certain cultural categories, and leaving out the cultural minority within a racial demographic that wants more representation.

This is upsetting to some people when, for example, Facebook does this and allows advertisers to target things based on “ethnic affinity”. Presumably, Facebook is doing just this kind of factor analysis when they identify these categories.

Arguably, that’s not what this sort of science is for. But the fact that the objection seems pertinent is an informative intuition in its own right.

Maybe the right framework for understanding why this is problematic is Omi and Winant’s racial formation theory (2014). I’m just getting into this theory recently, at the recommendation of Bruce Haynes, who I look up to as an authority on race in America. According to racial projects theory, racial categories are stable because they include both representations of groups of people as having certain qualities and social structures controlling the distribution of resources. So, the white/black divide in the U.S. is both racial stereotypes and segregating urban policy, because the divide is stable because of how the material and cultural factors reinforce each other.

This view is enlightening because it helps explain why hereditary phenotype, representations of people based on hereditary phenotype, requests for people to identify with a race even when this may not make any sense, policies about inheritance and schooling, etc. all are part of the same complex. When we were setting out to develop the method described above, we were trying to correct for a sampling bias in media while testing for the distribution of culture across some objectively determinable population variables. But the objective qualities (such as zip code) are themselves functions of the cultural traits when considered over the course of time. In short, our model, which just tabulates individual differences without looking at temporal mechanisms, is naive.

But it’s a start, if only to an interesting discussion.

References

Marsden, Peter V., and Joseph F. Swingle. “Conceptualizing and measuring culture in surveys: Values, strategies, and symbols.” Poetics 22.4 (1994): 269-289.

Omi, Michael, and Howard Winant. Racial formation in the United States. Routledge, 2014.

Tufekci, Zeynep. “Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls.” ICWSM 14 (2014): 505-514.

thinking about meritocracy in open source communities

There has been a trend in open source development culture over the past ten years or so. It is the rejection of ‘meritocracy’. Just now, I saw this Post-Meritocracy Manifesto, originally created by Coraline Ada Ehmke. It is exactly what it sounds like: an explicit rejection of meritocracy, specifically in open source development. It captures a recent progressive wing of software development culture. It is attracting signatories.

I believe this is a “trend” because I’ve noticed a more subtle expression of similar ideas a few months ago. This came up when we were coming up with a Code of Conduct for BigBang. We wound up picking the Contributor Covenant Code of Conduct, though there’s still some open questions about how to integrate it with our Governance policy.

This Contributor Covenant is widely adopted and the language of it seems good to me. I was surprised though when I found the rationale for it specifically mentioned meritocracy as a problem the code of conduct was trying to avoid:

Marginalized people also suffer some of the unintended consequences of dogmatic insistence on meritocratic principles of governance. Studies have shown that organizational cultures that value meritocracy often result in greater inequality. People with “merit” are often excused for their bad behavior in public spaces based on the value of their technical contributions. Meritocracy also naively assumes a level playing field, in which everyone has access to the same resources, free time, and common life experiences to draw upon. These factors and more make contributing to open source a daunting prospect for many people, especially women and other underrepresented people.

If it looks familiar, it may be because it was written by the same author, Coraline Ada Ehmke.

I have to admit that though I’m quite glad that we have a Code of Conduct now in BigBang, I’m uncomfortable with the ideological presumptions of its rationale and the rejection of ‘meritocracy’. There is a lot packed into this paragraph that is open to productive disagreement and which is not necessary for a commitment to the general point that harassment is bad for an open source community.

Perhaps this would be easier for me to ignore if this political framing did not mirror so many other political tensions today, and if open source governance were not something I’ve been so invested in understanding. I’ve taught a course on open source management, and BigBang spun out of that effort as an experiment in scientific analysis of open source communities. I am, I believe, deep in on this topic.

So what’s the problem? The problem is that I think there’s something painfully misaligned about criticism of meritocracy in culture at large and open source development, which is a very particular kind of organizational form. There is also perhaps a misalignment between the progressive politics of inclusion expressed in these manifestos and what many open source communities are really trying to accomplish. Surely there must be some kind of merit that is not in scare quotes, or else there would not be any good open source software to use a raise a fuss about.

Though it does not directly address the issue, I’m reminded of an old email discussion on the Numpy mailing list that I found when I was trying to do ethnographic work on the Scientific Python community. It was a response by John Hunter, the creator of Matplotlib, in response to concerns raised when Travis Oliphant, the leader of NumPy, started Continuum Analytics and there were concerns about corporate control over NumPy. Hunter quite thoughtfully, in my opinion, debunked the idea that open source governance should be a ‘democracy’, like many people assume institutions ought to be by default. After a long discussion about how Travis had great merit as a leader, he argued:

Democracy is something that many of us have grown up by default to consider as the right solution to many, if not most or, problems of governance. I believe it is a solution to a specific problem of governance. I do not believe democracy is a panacea or an ideal solution for most problems: rather it is the right solution for which the consequences of failure are too high. In a state (by which I mean a government with a power to subject its people to its will by force of arms) where the consequences of failure to submit include the death, dismemberment, or imprisonment of dissenters, democracy is a safeguard against the excesses of the powerful. Generally, there is no reason to believe that the simple majority of people polled is the “best” or “right” answer, but there is also no reason to believe that those who hold power will rule beneficiently. The democratic ability of the people to check to the rule of the few and powerful is essential to insure the survival of the minority.

In open source software development, we face none of these problems. Our power to fork is precisely the power the minority in a tyranical democracy lacks: noone will kill us for going off the reservation. We are free to use the product or not, to modify it or not, to enhance it or not.

The power to fork is not abstract: it is essential. matplotlib, and chaco, both rely *heavily* on agg, the Antigrain C++ rendering library. At some point many years ago, Maxim, the author of Agg, decided to change the license of Agg (circa version 2.5) to GPL rather than BSD. Obviously, this was a non-starter for projects like mpl, scipy and chaco which assumed BSD licensing terms. Unfortunately, Maxim had a new employer which appeared to us to be dictating the terms and our best arguments fell on deaf ears. No matter: mpl and Enthought chaco have continued to ship agg 2.4, pre-GPL, and I think that less than 1% of our users have even noticed. Yes, we forked the project, and yes, noone has noticed. To me this is the ultimate reason why governance of open source, free projects does not need to be democratic. As painful as a fork may be, it is the ultimate antidote to a leader who may not have your interests in mind. It is an antidote that we citizens in a state government may not have.

It is true that numpy exists in a privileged position in a way that matplotlib or scipy does not. Numpy is the core. Yes, Continuum is different than STScI because Travis is both the lead of Numpy and the lead of the company sponsoring numpy. These are important differences. In the worst cases, we might imagine that these differences will negatively impact numpy and associated tools. But these worst case scenarios that we imagine will most likely simply distract us from what is going on: Travis, one of the most prolific and valuable contributers to the scientific python community, has decided to refocus his efforts to do more. And that is a very happy moment for all of us.

This is a nice articulation of how forking, not voting, is the most powerful governance mechanism in open source development, and how it changes what our default assumptions about leadership ought to be. A critical but I think unacknowledged question is to how the possibility of forking interacts with the critique of meritocracy in organizations in general, and specifically what that means for community inclusiveness as a goal in open source communities. I don’t think it’s straightforward.

Note: Nick Doty has written a nice response to this on his blog.

Inequality perceived through implicit factor analysis and its implications for emergent social forms

Vox published an interview with Keith Payne, author of The Broken Ladder.

My understanding is that the thesis of the book is that income inequality has a measurable effect on public health, especially certain kinds of chronic illnesses. The proposed mechanism for this effect is the psychological state of those perceiving themselves to be relatively worse off. This is a hardwired mechanism, it would seem, and one that is being turned on more and more by socioeconomic conditions today.

I’m happy to take this argument for granted until I hear otherwise. I’m interested in (and am jotting notes down here, not having read the book) the physics of this mechanism. It’s part of a larger puzzle about social forms, emergent social properties, and factor analysis that I’ve written about it some other posts.

Here’s the idea: income inequality is a very specific kind of social metric and not one that is easy to directly perceive. Measuring it from tax records, which short be straightforward, is fraught with technicalities. Therefore, it is highly implausible that direct perception of this metric is what causes the psychological impact of inequality.

Therefore, there must be one or more mediating factors between income inequality as an economic fact and psychological inequality as a mental phenomenon. Let’s suppose–because it’s actually what we should see as a ‘null hypothesis’–that there are many, many factors linking these phenomena. Some may be common causes of income inequality and psychological inequality, such as entrenched forms of social inequality that prevent equal access to resources and are internalized somehow. Others may be direct perception of the impact of inequality, such as seeing other people flying in higher class seats, or (ahem) hearing other people talk about flying at all. And yet we seem comfortable deriving from this very complex mess a generalized sense of inequality and its impact, and now that’s one of the most pressing political topics today.

I want to argue that when a person perceives inequality in a general way, they are in effect performing a kind of factor analysis on their perceptions of other people. When we compare ourselves with others, we can do so on a large number of dimensions. Cognitively, we can’t grok all of it–we have to reduce the feature space, and so we come to understand the world through a few blunt indicators that combine many other correlated data points into one.

These blunt categories can suggest that there is structure in the world that isn’t really there, but rather is an artifact of constraints on human perception and cognition. In other words, downward causation would happen in part through a dimensionality reduction of social perception.

On the other hand, if those constraints are regular enough, they may in turn impose a kind of structure on the social world (upward causation). If downward causation and upward causation reinforced each other, then that would create some stable social conditions. But there’s also no guarantee that stable social perceptions en masse track the real conditions. There may be systematic biases.

I’m not sure where this line of inquiry goes, to be honest. It needs more work.

General intelligence, social privilege, and causal inference from factor analysis

I came upon this excellent essay by Cosma Shalizi about how factor analysis has been spuriously used to support the scientific theory of General Intelligence (i.e., IQ). Shalizi, if you don’t know, is one of the best statisticians around. He writes really well and isn’t afraid to point out major blunders in things. He’s one of my favorite academics, and I don’t think I’m alone in this assessment.

First, a motive: Shalizi writes this essay because he thinks the scientific theory of General Intelligence, or a g factor that is some real property of the mind, is wrong. This theory is famous because (a) a lot of people DO believe in IQ as a real feature of the mind, and (b) a significant percentage of these people believe that IQ is hereditary and correlated with race, and (c) the ideas in (b) are used to justify pernicious and unjust social policy. Shalizi, being a principled statistician, appears to take scientific objection to (a) independently of his objection to (c), and argues persuasively that we can reject (a). How?

Shalizi’s point is that the general intelligence factor g is a latent variable that was supposedly discovered using a factor analysis of several different intelligence tests that were supposed to be independent of each other. You can take the data from these data sets and do a dimensionality reduction (that’s what factor analysis is) and get something that looks like a single factor, just as you can take a set of cars and do a dimensionality reduction and get something that looks like a single factor, “size”. The problem is that “intelligence”, just like “size”, can also be a combination of many other factors that are only indirectly associated with each other (height, length, mass, mass of specific components independent of each other, etc.). Once you have many different independent factors combining into one single reduced “dimension” of analysis, you no longer have a coherent causal story of how your general latent variable caused the phenomenon. You have, effectively, correlation without demonstrated causation and, moreover, the correlation is a construct of your data analysis method, and so isn’t really even telling you what correlations normally tell you.

To put it another way: the fact that some people seem to be generally smarter than other people can be due to thousands of independent factors that happen to combine when people apply themselves to different kinds of tasks. If some people were NOT seeming generally smarter than others, that would allow you to reject the hypothesis that there was general intelligence. But the mere presence of the aggregate phenomenon does not prove the existence of a real latent variable. In fact, Shalizi goes on to say, when you do the right kinds of tests to see if there really is a latent factor of ‘general intelligence’, you find that there isn’t any. And so it’s just the persistent and possibly motivated interpretation of the observational data that allows the stubborn myth of general intelligence to continue.

Are you following so far? If you are, it’s likely because you were already skeptical of IQ and its racial correlates to begin with. Now I’m going to switch it up though…

It is fairly common for educated people in the United States (for example) to talk about “privilege” of social groups. White privilege, male privilege–don’t tell me you haven’t at least heard of this stuff before; it is literally everywhere on the center-left news. Privilege here is considered to be a general factor that adheres in certain social groups. It is reinforced by all manner of social conditioning, especially through implicit bias in individual decision-making. This bias is so powerful it extends not to just cases of direct discrimination but also in cases where discrimination happens in a mediated way, for example through technical design. The evidence for these kinds of social privileging effects is obvious: we see inequality everywhere, and we can who is more powerful and benefited by the status quo and who isn’t.

You see where this is going now. I have the momentum. I can’t stop. Here it goes: Maybe this whole story about social privilege is as spuriously supported as the story about general intelligence? What if both narratives were over-interpretations of data that serve a political purpose, but which are not in fact based on sound causal inference techniques?

How could this be? Well, we might gather a lot of data about people: wealth, status, neighborhood, lifespan, etc. And then we could run a dimensionality reduction/factor analysis and get a significant factor that we could name “privilege” or “power”. Potentially that’s a single, real, latent variable. But also potentially it’s hundreds of independent factors spuriously combined into one. It would probably, if I had to bet on it, wind up looking a lot like the factor for “general intelligence”, which plays into the whole controversy about whether and how privilege and intelligence get confused. You must have heard the debates about, say, representation in the technical (or other high-status, high-paying) work force? One side says the smart people get hired; the other side say it’s the privileged (white male) people that get hired. Some jerk suggests that maybe the white males are smarter, and he gets fired. It’s a mess.

I’m offering you a pill right now. It’s not the red pill. It’s not the blue pill. It’s some other colored pill. Green?

There is no such thing as either general intelligence or group based social privilege. Each of these are the results of sloppy data compression over thousands of factors with a loose and subtle correlational structure. The reason why patterns of social behavior that we see are so robust against interventions is that each intervention can work against only one or two of these thousands of factors at a time. Discovering the real causal structure here is hard partly because the effect sizes are very small. Anybody with a simple explanation, especially a politically convenient explanation, is lying to you but also probably lying to themselves. We live in a complex world that resists our understanding and our actions to change it, though it can be better understood and changed through sound statistics. Most people aren’t bothering to do this, and that’s why the world is so dumb right now.

Goodbye, TheListserve!

Today I got an email I never thought I’d get: a message from the creators of TheListserve saying they were closing down the service after over 6 years.

TheListserve was a fantastic idea: it was a mailing list that allowed one person, randomly selected from the subscribers each day, to email everyone else.

It was an experiment in creating a different kind of conversational space on-line. And it worked great! Tens of thousands of subscribers, really interesting content–a space unlike most others in social media. You really did get a daily email with what some random person thought was the most interesting thing they had to say.

I was inspired enough by TheListserve to write a Twitter bot based on similar principles, TheTweetserve. Maybe the Twitter bot was also inspired by Habermas. It was not nearly as successful or interesting as TheListserve, for reasons that you could deduce if you thought about it.

Six years ago, “The Internet” was a very different imaginary. There was this idea that a lightweight intervention could capture some of the magic of serendipity that scale and connection had to offer, and that this was going to be really, really big.

It was, I guess, but then the charm wore off.

What’s happened now, I think, is that we’ve been so exposed to connection and scale that novelty has worn off. We now find ourselves exposed on-line mainly to the imposing weight of statistical aggregates and regressions to the mean. After years of messages to TheListserve, it started, somehow, to seem formulaic. You would get honest, encouraging advice, or a self-promotion. It became, after thousands of emails, a genre in itself.

I wonder if people who are younger and less jaded than I am are still finding and creating cool corners of the Internet. What I hear about more and more now are the ugly parts; they make the news. The Internet used to be full of creative chaos. Now it is so heavily instrumented and commercialized I get the sense that the next generation will see it much like I saw radio or television when I was growing up: as a medium dominated by companies, large and small. Something you had to work hard to break into as a professional choice or otherwise not at all.

“Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics” <– My dissertation

In the last two weeks, I’ve completed, presented, and filed my dissertation, and commenced as a doctor of philosophy. In a word, I’ve PhinisheD!

The title of my dissertation is attention-grabbing, inviting, provocative, and impressive:

“Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics”

If you’re reading this, you are probably wondering, “How can I drop everything and start reading that hot dissertation right now?”

Look no further: here is a link to the PDF.

You can also check out this slide deck from my “defense”. It covers the highlights.

I’ll be blogging about this material as I break it out into more digestible forms over time. For now, I’m obviously honored by any interest anybody takes in this work and happy to answer questions about it.