Polygraph‘s Hanah Anderson and Matt Daniels undertook a massive analysis of the dialogue of approximately 2,000 films, counting those characters who spoke at least 100 words. With the data, they’ve producing a series of visuals that powerfully illustrate male dominance in the American film industry.

We’ve seen data like this before and it tells the same disturbing story: across the industry, whatever the sub-genre, men and their voices take center stage.

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They have some other nice insights, too, like the silencing of women as they get older and the enhancing of men’s older voices.

But knowledge is power. My favorite thing about this project is that it enables any of us — absolutely anyone — to look up the gender imbalance in dialogue in any of those 2,000 movies. This means that you can know ahead of time how well women’s and men’s voices are represented and decide whether to watch. The dialogue in Adaptation, for example, is 70% male; Good Will Hunting, 85% male; The Revenant, 100% male.

We could even let the site choose the movies for us. Anderson and Daniels include a convenient dot graph that spans the breadth of inclusion, with each dot representing a movie. You can just click on the distribution that appeals to you and choose a movie from there. Clueless, Gosford Park, and The Wizard of Oz all come in at a perfect 50/50 split. Or, you can select a decade, genre, and gender balance and get suggestions.

Polygraph has enabled us to put our money where our principles are. If enough of us decide that we won’t buy any movie that tilts too far male, it would put pressure on filmmakers to make movies that better reflected real life. This data makes it possible to do just that.

Lisa Wade is a professor at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. Find her on TwitterFacebook, and Instagram.

2 (1)There was a great article in The Nation last week about social media and ad hoc credit scoring. Can Facebook assign you a score you don’t know about but that determines your life chances?

Traditional credit scores like your FICO or your Beacon score can determine your life chances. By life chances, we generally mean how much mobility you will have. Here, we mean a number created by third party companies often determines you can buy a house/car, how much house/car you can buy, how expensive buying a house/car will be for you. It can mean your parents not qualifying to co-sign a student loan for you to pay for college. These are modern iterations of life chances and credit scores are part of it.

It does not seem like Facebook is issuing a score, or a number, of your creditworthiness per se. Instead they are limiting which financial vehicles and services are offered to you in ads based on assessments of your creditworthiness.

One of the authors of The Nation piece (disclosure: a friend), Astra Taylor, points out how her Facebook ads changed when she started using Facebook to communicate with student protestors from for-profit colleges. I saw the same shift when I did a study of non-traditional students on Facebook.

You get ads like this one from DeVry:

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Although, I suspect my ads were always a little different based on my peer and family relations. Those relations are majority black. In the U.S. context that means it is likely that my social network has a lower wealth and/or status position as read through the cumulative historical impact of race on things like where we work, what jobs we have, what schools we go to, etc. But even with that, after doing my study, I got every for-profit college and “fix your student loan debt” financing scheme ad known to man.

Whether or not I know these ads are scams is entirely up to my individual cultural capital. Basically, do I know better? And if I do know better, how do I come to know it?

I happen to know better because I have an advanced education, peers with advanced educations and I read broadly. All of those are also a function of wealth and status. I won’t draw out the causal diagram I’ve got brewing in my mind but basically it would say something like, “you need wealth and status to get advantageous services offered you on the social media that overlays our social world and you need proximity wealth and status to know when those services are advantageous or not”.

It is in interesting twist on how credit scoring shapes life chances. And it runs right through social media and how a “personalized” platform can never be democratizing when the platform operates in a society defined by inequalities.

I would think of three articles/papers in conversation if I were to teach this (hint, I probably will). Healy and Fourcade on how credit scoring in a financialized social system shapes life chances is a start:

providers have learned to tailor their products in specific ways in an effort to maximize rents, transforming the sources and forms of inequality in the process.

And then Astra Taylor and Jathan Sadowski’s piece in The Nation as a nice accessible complement to that scholarly article:

Making things even more muddled, the boundary between traditional credit scoring and marketing has blurred. The big credit bureaus have long had sidelines selling marketing lists, but now various companies, including credit bureaus, create and sell “consumer evaluation,” “buying power,” and “marketing” scores, which are ingeniously devised to evade the FCRA (a 2011 presentation by FICO and Equifax’s IXI Services was titled “Enhancing Your Marketing Effectiveness and Decisions With Non-Regulated Data”). The algorithms behind these scores are designed to predict spending and whether prospective customers will be moneymakers or money-losers. Proponents claim that the scores simply facilitate advertising, and that they’re not used to approve individuals for credit offers or any other action that would trigger the FCRA. This leaves those of us who are scored with no rights or recourse.

And then there was Quinn Norton this week on The Message talking about her experiences as one of those marketers Taylor and Sadowski allude to. Norton’s piece summarizes nicely how difficult it is to opt-out of being tracked, measured and sold for profit when we use the Internet:

I could build a dossier on you. You would have a unique identifier, linked to demographically interesting facts about you that I could pull up individually or en masse. Even when you changed your ID or your name, I would still have you, based on traces and behaviors that remained the same — the same computer, the same face, the same writing style, something would give it away and I could relink you. Anonymous data is shockingly easy to de-anonymize. I would still be building a map of you. Correlating with other databases, credit card information (which has been on sale for decades, by the way), public records, voter information, a thousand little databases you never knew you were in, I could create a picture of your life so complete I would know you better than your family does, or perhaps even than you know yourself.

It is the iron cage in binary code. Not only is our social life rationalized in ways even Weber could not have imagined but it is also coded into systems in ways difficult to resist, legislate or exert political power.

Gaye Tuchman and I talk about this full rationalization in a recent paper on rationalized higher education. At our level of analysis, we can see how measurement regimes not only work at the individual level but reshape entire institutions. Of recent changes to higher education (most notably Wisconsin removing tenure from state statute causing alarm about the role of faculty in public higher education) we argue that:

In short, the for-profit college’s organizational innovation lies not in its growth but in its fully rationalized educational structure, the likes of which being touted in some form as efficiency solutions to traditional colleges who have only adopted these rationalized processes piecemeal.

And just like that we were back to the for-profit colleges that prompted Taylor and Sadowski’s article in The Nation.

Efficiencies. Ads. Credit scores. Life chances. States. Institutions. People. Inequality.

And that is how I read. All of these pieces are woven together and its a kind of (sad) fun when we can see how. Contemporary inequalities run through rationalized systems that are being perfected on social media (because its how we social), given form through institutions, and made invisible in the little bites of data we use for critical minutiae that the Internet has made it difficult to do without.

Tressie McMillan Cottom is an assistant professor of sociology at Virginia Commonwealth University.  Her doctoral research is a comparative study of the expansion of for-profit colleges.  You can follow her on twitter and at her blog, where this post originally appeared.

Flashback Friday.

A study published in 2001, to which I was alerted by Family Inequality, asked undergraduate college students their favorite color and presented the results by sex.  Men’s favorites are on the left, women’s on the right:

The article is a great example of the difference between research findings and the interpretation of those findings.  For example, this is how I would interpret it:

Today in the US, but not elsewhere and not always, blue is gendered male and pink gendered female.  We might expect, then, that men would internalize a preference for blue and women a preference for pink.  We live, however, in an androcentric society that values masculinity over femininity.  This rewards the embracing of masculinity by both men and women (making it essentially compulsory for men) and stigmatizes the embracing of femininity (especially for men).

We might expect, then, that men would comfortably embrace a love of blue (blue = masculinity = good), while many women will have a troubled relationship to pink (pink = femininity = devalued, but encouraged for women) and gravitate to blue and all of the good, masculine meaning it offers.

That’s how I’d interpret it.

Here’s how the authors of the study interpreted it:

…we are inclined to suspect the involvement of neurohormonal factors. Studies of rats have found average sex differences in the number of neurons comprising various parts of the visual cortex. Also, gender differences have been found in rat preferences for the amount of sweetness in drinking water. One experiment demonstrated that the sex differences in rat preferences for sweetness was eliminated by depriving males of male-typical testosterone levels in utero. Perhaps, prenatal exposure to testosterone and other sex hormones operates in a similar way to “bias” preferences for certain colors in humans.

Go figure.

Important lesson here: data never stands alone. It must always be interpreted.

Originally posted in 2010.

Lisa Wade is a professor at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. Find her on TwitterFacebook, and Instagram.

Daniel Drezner once wrote about how international relations scholars would react to a zombie epidemic. Aside from the sheer fun of talking about something as silly as zombies, it had much the same illuminating satiric purpose as “how many X does it take to screw in a lightbulb” jokes. If you have even a cursory familiarity with the field, it is well worth reading.

Here’s my humble attempt to do the same for several schools within sociology.

Public Opinion. Consider the statement that “Zombies are a growing problem in society.” Would you:

  1. Strongly disagree
  2. Somewhat disagree
  3. Neither agree nor disagree
  4. Somewhat agree
  5. Strongly agree
  6. Um, how do I know you’re really with NORC and not just here to eat my brain?

Criminology. In some areas (e.g., Pittsburgh, Raccoon City), zombification is now more common that attending college or serving in the military and must be understood as a modal life course event. Furthermore, as seen in audit studies employers are unwilling to hire zombies and so the mark of zombification has persistent and reverberating effects throughout undeath (at least until complete decomposition and putrefecation). However, race trumps humanity as most employers prefer to hire a white zombie over a black human.

Cultural toolkit. Being mindless, zombies have no cultural toolkit. Rather the great interest is understanding how the cultural toolkits of the living develop and are invoked during unsettled times of uncertainty, such as an onslaught of walking corpses. The human being besieged by zombies is not constrained by culture, but draws upon it. Actors can draw upon such culturally-informed tools as boarding up the windows of a farmhouse, shotgunning the undead, or simply falling into panicked blubbering.

Categorization. There’s a kind of categorical legitimacy problem to zombies. Initially zombies were supernaturally animated dead, they were sluggish but relentlessness, and they sought to eat human brains. In contrast, more recent zombies tend to be infected with a virus that leaves them still living in a biological sense but alters their behavior so as to be savage, oblivious to pain, and nimble. Furthermore, even supernatural zombies are not a homogenous set but encompass varying degrees of decomposition. Thus the first issue with zombies is defining what is a zombie and if it is commensurable with similar categories (like an inferius in Harry Potter). This categorical uncertainty has effects in that insurance underwriters systematically undervalue life insurance policies against monsters that are ambiguous to categorize (zombies) as compared to those that fall into a clearly delineated category (vampires).

Neo-institutionalism. Saving humanity from the hordes of the undead is a broad goal that is easily decoupled from the means used to achieve it. Especially given that human survivors need legitimacy in order to command access to scarce resources (e.g., shotgun shells, gasoline), it is more important to use strategies that are perceived as legitimate by trading partners (i.e., other terrified humans you’re trying to recruit into your improvised human survival cooperative) than to develop technically efficient means of dispatching the living dead. Although early on strategies for dealing with the undead (panic, “hole up here until help arrives,” “we have to get out of the city,” developing a vaccine, etc) are practiced where they are most technically efficient, once a strategy achieves legitimacy it spreads via isomorphism to technically inappropriate contexts.

Population ecology. Improvised human survival cooperatives (IHSC) demonstrate the liability of newness in that many are overwhelmed and devoured immediately after formation. Furthermore, IHSC demonstrate the essentially fixed nature of organizations as those IHSC that attempt to change core strategy (eg, from “let’s hole up here until help arrives” to “we have to get out of the city”) show a greatly increased hazard for being overwhelmed and devoured.

Diffusion. Viral zombieism (e.g. Resident Evil, 28 Days Later) tends to start with a single patient zero whereas supernatural zombieism (e.g. Night of the Living Dead, the “Thriller” video) tends to start with all recently deceased bodies rising from the grave. By seeing whether the diffusion curve for zombieism more closely approximates a Bass mixed-influence model or a classic s-curve we can estimate whether zombieism is supernatural or viral, and therefore whether policy-makers should direct grants towards biomedical labs to develop a zombie vaccine or the Catholic Church to give priests a crash course in the neglected art of exorcism. Furthermore, marketers can plug plausible assumptions into the Bass model so as to make projections of the size of the zombie market over time, and thus how quickly to start manufacturing such products as brain-flavored Doritos.

Social movements. The dominant debate is the extent to which anti-zombie mobilization represents changes in the political opportunity structure brought on by complete societal collapse as compared to an essentially expressive act related to cultural dislocation and contested space. Supporting the latter interpretation is that zombie hunting militias are especially likely to form in counties that have seen recent increases in immigration. (The finding holds even when controlling for such variables as gun registrations, log distance to the nearest army administered “safe zone,” etc.).

Family. Zombieism doesn’t just affect individuals, but families. Having a zombie in the family involves an average of 25 hours of care work per week, including such tasks as going to the butcher to buy pig brains, repairing the boarding that keeps the zombie securely in the basement and away from the rest of the family, and washing a variety of stains out of the zombie’s tattered clothing. Almost all of this care work is performed by women and very little of it is done by paid care workers as no care worker in her right mind is willing to be in a house with a zombie.

Applied micro-economics. We combine two unique datasets, the first being military satellite imagery of zombie mobs and the second records salvaged from the wreckage of Exxon/Mobil headquarters showing which gas stations were due to be refueled just before the start of the zombie epidemic. Since humans can use salvaged gasoline either to set the undead on fire or to power vehicles, chainsaws, etc., we have a source of plausibly exogenous heterogeneity in showing which neighborhoods were more or less hospitable environments for zombies. We show that zombies tended to shuffle towards neighborhoods with low stocks of gasoline. Hence, we find that zombies respond to incentives (just like school teachers, and sumo wrestlers, and crack dealers, and realtors, and hookers, …).

Grounded theory. One cannot fully appreciate zombies by imposing a pre-existing theoretical framework on zombies. Only participant observation can allow one to provide a thick description of the mindless zombie perspective. Unfortunately scientistic institutions tend to be unsupportive of this kind of research. Major research funders reject as “too vague and insufficiently theory-driven” proposals that describe the intention to see what findings emerge from roaming about feasting on the living. Likewise IRB panels raise issues about whether a zombie can give informed consent and whether it is ethical to kill the living and eat their brains.

Ethnomethodology. Zombieism is not so much a state of being as a set of practices and cultural scripts. It is not that one is a zombie but that one does being a zombie such that zombieism is created and enacted through interaction. Even if one is “objectively” a mindless animated corpse, one cannot really be said to be fulfilling one’s cultural role as a zombie unless one shuffles across the landscape in search of brains.

Conversation Analysis.2 (1)

Cross-posted at Code and Culture.

Gabriel Rossman is a professor of sociology at UCLA. His research addresses culture and mass media, especially pop music radio and Hollywood films, with the aim of understanding diffusion processes. You can follow him at Code and Culture.

In the 6-minute video below, Stanford sociologist Aliya Saperstein discusses her research showing that the perception of other peoples’ race is shaped by what we know about them. She uses data collected through a series of in-person interviews in which interviewers sit down with respondents several times over many years, learn about what’s happened and, among other things, make a judgment call as to their race. You may be surprised how often racial designations. In one of her samples, 20% of respondents were inconsistently identified, meaning that they were given different racial classifications by different interviewers at least once.

Saperstein found that a person judged as white in an early interview was more likely to be marked as black in a later interview if they experienced a life event that is stereotypically associated with blackness, like imprisonment or unemployment.

She and some colleagues also did an experiment, asking subjects to indicate whether people with black, white, and ambiguous faces dressed in a suit or a blue work shirt were white or black. Tracing their mouse paths, it was clear that the same face in a suit was more easily categorized as white than the one in a work shirt.

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Race is a social construction, not just in the sense that we made it up, but in that it’s flexible and dependent on status as well as phenotype.

She finishes with the observation that, while phenotype definitely impacts a person’s life chances, we also need to be aware that differences in education, income, and imprisonment reflect not only bias against phenotype, but the fact that success begets whiteness. And vice versa.

Watch the whole thing here:

Lisa Wade is a professor at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. Find her on TwitterFacebook, and Instagram.

Flashback Friday.

In the talk embedded below, psychologist and behavioral economist Dan Ariely asks the question: How many of our decisions are based on our own preferences and how many of them are based on how our options are constructed? His first example regards willingness to donate organs. The figure below shows that some countries in Europe are very generous with their organs and other countries not so much.

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A cultural explanation, Ariely argues, doesn’t make any sense because very similar cultures are on opposite sides: consider Sweden vs. Denmark, Germany vs. Austria, and the Netherlands vs. Belgium.

What makes the difference then? It’s the wording of the question. In the generous countries the question is worded so as to require one to check the box if one does NOT want to donate:

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In the less generous countries, it’s the opposite. The question is worded so as to require one to check the box if one does want to donate:

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Lesson: The way the option is presented to us heavily influences the likelihood that we will or will not do something as important as donating our organs.

For more, and more great examples, watch the whole video:

Originally posted in 2010.

Lisa Wade is a professor at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. Find her on TwitterFacebook, and Instagram.

The margin of error is getting more attention than usual in the news. That’s not saying much since it’s usually a tiny footnote, like those rapidly muttered disclaimers in TV ads (“Offer not good mumble mumble more than four hours mumble mumble and Canada”). Recent headlines proclaim, “Trump leads Bush…” A paragraph or two in, the story will report that in the recent poll Trump got 18% and Bush 15%.  That difference is well within the margin of error, but you have to listen closely to hear that. Most people usually don’t want to know about uncertainty and ambiguity.

What’s bringing uncertainty out of the closest now is the upcoming Republican presidential debate. The Fox-CNN-GOP axis has decided to split the field of presidential candidates in two based on their showing in the polls. The top ten will be in the main event. All other candidates – currently Jindal, Santorum, Fiorina, et al. – will be relegated to the children’s table, i.e., a second debate a month later and at the very unprime hour of 5 p.m.

But is Rick Perry’s 4% in a recent poll (419 likely GOP voters) really in a different class than Bobby Jindal’s 25? The margin of error that CNN announced in that survey was a confidence interval of  +/- 5.  Here’s the box score.

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Jindal might argue that, with a margin of error of 5 points, his 2% might actually be as high as 7%, which would put him in the top tier.He might argue that, but he shouldn’t.  Downplaying the margin of error makes a poll result seem more precise than it really is, but using that one-interval-fits-all number of five points understates the precision. That’s because the margin of error depends on the percent that a candidate gets. The confidence interval is larger for proportions near 50%, smaller for proportions at the extreme.

Just in case you haven’t taken the basic statistics course, here is the formula.

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The   (pronounced “pee hat”) is the proportion of the sample who preferred each candidate. For the candidate who polled 50%, the numerator of the fraction under the square root sign will be 0.5 (1-0.5) = .25.  That’s much larger than the numerator for the 2% candidate:  0.02 (1-0.02) = .0196.*Multiplying by the 1.96, the 50% candidate’s margin of error with a sample of 419 is +/- 4.8. That’s the figure that CNN reported. But plug in Jindal’s 2%, and the result is much less: +/- 1.3.  So, there’s a less than one in twenty chance that Jindal’s true proportion of support is more than 3.3%.

Polls usually report their margin of error based on the 50% maximum. The media reporting the results then use the one-margin-fits-all assumption – even NPR. Here is their story from May 29 with the headline “The Math Problem Behind Ranking The Top 10 GOP Candidates”:

There’s a big problem with winnowing down the field this way: the lowest-rated people included in the debate might not deserve to be there.

The latest GOP presidential poll, from Quinnipiac, shows just how messy polling can be in a field this big. We’ve put together a chart showing how the candidates stack up against each other among Republican and Republican-leaning voters — and how much their margins of error overlap.

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The NPR writer, Danielle Kurtzleben, does mention that “margins might be a little smaller at the low end of the spectrum,” but she creates a graph that ignores that reality.The misinterpretation of presidential polls is nothing new.  But this time that ignorance will determine whether a candidate plays to a larger or smaller TV audience.

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* There are slightly different formulas for calculating the margin of error for very low percentages.  The Agresti-Coull formula gives a confidence interval even if there are zero Yes responses. (HT: Andrew Gelman)

Originally posted at Montclair SocioBlog.

Jay Livingston is the chair of the Sociology Department at Montclair State University. You can follow him at Montclair SocioBlog or on Twitter.

 PhD Comics, via Missives from Marx and Dmitriy T.M.