How feminism has made me a better scientist

Feminism is not a branch of science. It is not a set of testable propositions about the observable world, nor is it any single research method. From my own perspective, feminism is a political movement associated with successes such as votes for women, setbacks such as the failed Equal Rights Amendment, and continuing struggles in areas ranging from reproductive freedom to equality in the workplace. Feminism is also a way of looking at the world through awareness of the social and political struggles of women, historically and in the present. And others will define feminism in other ways.

How is this relevant to the practice of science? As a researcher in statistical methods and applications, and I have found feminism to help me do better science. I make this claim based on various experiences in my work.

To start with, statistics is all about learning from data. Statisticians from Florence Nightingale and Francis Galton through Hans Rosling and Nate Silver have discovered unexpected patterns using mathematical modeling and data visualization. What does that have to do with feminism? Feminism is about a willingness to question the dominant, tacitly accepted ideology. This is essential for science. What is labeled “non-ideological” basically means the dominant ideology is accepted without thought. As Angela Davis said in her lecture, Feminism and Abolition, “feminist methodologies impel us to explore connections that are not always apparent. And they drive us to inhabit contradictions and discover what is productive in these contradictions. Feminism insists on methods of thought and action that urge us to think about things together that appear to be separate, and to disaggregate things that appear to naturally belong together.”

Along with questioning the dominant, tacitly accepted ideology is the need to recognize this ideology. This is related to the idea that a valuable characteristic of a scientist is a willingness to be disturbed. We learn from anomalies (see discussion here), which requires recognizing how a given observation or story is anomalous (that is, anomalous with respect to what expectations, exactly?), which in turn is more effective if one can identify the substrates of theories that determine our expectations. An example from our statistical work is our research using the Xbox survey to reveal that apparent swings in public opinion can largely be explained by variations in nonresponse.

On a more specific level, feminism can make us aware of work where the male perspective is unthinkingly taken as a baseline. For example, a paper was released a few years ago presenting survey data from the United States showing that parents of girls were more likely to support the Republican party, compared to parents of boys. I’m not completely sure what to make of this finding—for one thing, an analysis a few years ago of data from Britain found the opposite pattern—but here I want to focus on the reception of this research claim. There’s something oddly asymmetrical about how these results were presented, both by the authors and in the media. Consider the following headlines: “The Effect of Daughters on Partisanship and Social Attitudes Toward Women,” “Does Having Daughters Make You More Republican?”, “Parents With Daughters Are More Likely To Be Republicans, Says New Study,” “Parents Of Daughters Lean Republican, Study Shows,” “The Daughter Theory: Does raising girls make parents conservative?” Here’s our question: Why is it all about “the effect of daughters”? Why not, Does having sons make you support the Democrats? This is not just semantics: the male-as-baseline perspective affects the explanations that are given for this pattern: Lots of discussion about how you, as a parent, might change your views of the world if you have a girl. But not so much about how you might change your views if you have a boy.

The fallacy here was that people were reasoning unidirectionally. In this case, the benefit of a feminist perspective is not political but rather just a recognition of multiple perspectives and social biases, recognizing that in this case the boy baby is considered a default. As the saying goes, the greatest trick the default ever pulled was convincing the world it didn’t exist.

To put it another way: it is not that feminism is some sort of superpower that allows one to consider alternatives to the existing defaults, it’s more that these alternatives are obvious and can only not be seen if you don’t allow yourself to look. Feminism is, for this purpose, not a new way of looking at the world; rather, it is a simple removal of blinders. But uncovering blind spots isn’t that simple, and can be quite powerful.

A broader point is that it’s hard to do good social science if you don’t understand the community you’re studying. The lesson from feminism is not just to avoid taking the male perspective for granted but more generally to recognize the perspective of outgroups. An example came up recently with the so-called gaydar study, a much-publicized paper demonstrating the ability of a machine learning algorithm to distinguish gay and straight faces based on photographs from dating sites. This study was hyped beyond any reasonable level. From a statistical perspective, it’s no surprise at all that two groups of people selected from different populations will differ from each other, and if you have samples from two different populations and a large number of cases, then you should be able to train an algorithm to distinguish them at some level of accuracy. The authors of the paper in question went way beyond this, though, claiming that their results “provide strong support for the PHT [prenatal hormone theory], which argues that same-gender sexual orientation stems from the underexposure of male fetuses and overexposure of female fetuses to prenatal androgens responsible for the sexual differentiation of faces, preferences, and behavior.” Also some goofy stuff about the fact that gay men in this particular sample are less likely to have beards. Beyond the purely statistical problems here is a conceptual error, which is to think of “gay faces” as some sort of innate property of gay people, rather than as cues that gays are deliberately sending to each other. The distinctive and noticeable characteristics of the subpopulation are the result of active choices by members of that group, not (as assumed in the paper under discussion) essential attributes derived from “nature” or “nurture.”

Looking from a different direction, feminism can make us suspicious of simplistic gender-essentialist ideas such as expressed in various papers that make use of schoolyard evolutionary biology—the idea that, because of evolution, all people are equivalent to all other people, except that all boys are different from all girls. It’s the attitude I remember from the grade school playground, in which any attribute of a person, whether it be how you walked or how you laughed or even how you held your arms when you were asked to look at your fingernails (really) were gender-typed. It’s gender and race essentialism. And when you combine it with what Tversky and Kahneman called “the law of small numbers” (the naive but common attitude that any underlying pattern should reproduce in any small sample) has led to endless chasing of noise in data analyses. In short, if you believe this sort of essentialism, you can find it just about anywhere you look, hence the value of a feminist perspective which reminds us of the history and fallacies of gender essentialism. Of course there are lots of systematic differences between boys and girls, and between men and women, that are not directly sex-linked. To be a feminist is not to deny these differences; rather, placing these differences within a larger context, and relating them to past and existing power imbalances, is part of what feminism is about.

Many examples of schoolyard evolutionary biology in published science will be familiar to regular readers of this blog: there’s the beauty-and-sex-ratio study, the ovulation-and-clothing study, the fat-arms-and-political attitudes study (a rare example that objectified men instead of women), the ovulation-and-voting study, and various others. Just to be clear: I’m not saying that the claims in those studies have to be wrong. All these claims, to my eyes, look crudely gender-essentialist, but that doesn’t mean they’re false, or that it was a bad idea to study them. No, all those studies had problems in their statistics (in the sense that poor understanding of statistical methods led researchers and observers to wrongly think that those data presented strong evidence in favor of those claims) and in their measurement (in that the collected data were too sparse and noisy to really have a chance of supplying the desired evidence).

A feminist perspective was helpful to me in unraveling these studies for several reasons. To start with, feminism gave me the starting point of skepticism regarding naive gender essentialism—or, I could say, it help keep me from being intimidated by weak theorizing coming from that direction. Second, feminism made me aware of multiple perspectives: if someone can hypothesize that prettier parents are more likely to produce girls, I can imagine an opposite just-so story that makes just as much sense. And, indeed, both stories could be true at different times and different places, which brings me to the third thing I bring from feminism: an awareness of variation. There’s no reason to think that a hypothesized effect will be consistent in magnitude or even sign for different people and under different conditions. Understanding this point is a start toward moving away from naive, one might say “scientistic,” views of what can be learned or proved from simple surveys or social experiments.

I consider myself a feminist but I understand that others have different political views, and I’m not trying to say that being a feminist is necessary for doing science. Of course not. Rather, my point is that I think the political and historical insights of feminism have made me a better statistician and scientist.

As I wrote a few years ago:

At some level, in this post I’m making the unremarkable point that each of us has a political perspective which informs our research in positive and negative ways. The reason that this particular example of the feminist statistician is interesting is that it’s my impression that feminism, like religion, is generally viewed as a generally anti-scientific stance. I think some of this attitude comes from some feminists themselves who are skeptical of science in that is a generally male-dominated institution that is in part used to continue male dominance of society, and it also comes from people who might say that reality has an anti-feminist bias.

We can try to step back and account for our own political and ethical positions when doing science, but at the same time we should be honest about the ways that our positions and experiences shape the questions we ask and produce insights.

Feminism, like religion or other social identifications, can be competitive with science or it can be collaborative. See, for example, the blog of Echidne for a collaborative approach. To the extent that feminism represents a set of tenets are opposed to reality, it could get in the way of scientific thinking, in the same way that religion would get in the way of scientific thinking if, for example, you tried to apply faith healing principles to do medical research. If you’re serious about science, though, I think of feminism (or, I imagine, Christianity, for example) as a framework rather than a theory—that is, as a way of interpreting the world, not as a set of positive statements. This is in the same way that I earlier wrote that racism is a framework, not a theory. Not all frameworks are equal; my point here is just that, if we’re used to thinking of feminism, or religion, as anti-scientific, it can be useful to consider ways in which these perspectives can help one’s scientific work.

All of this is just one perspective. I did get several useful comments and references from Shira Mitchell and Dan Simpson when preparing this post, but the particular stories and emphases are mine. One could imagine a whole series of such posts—“How feminism made me a worse scientist,” “How science has made me a better feminist,” “How Christianity has made me a better scientist,” and so forth—all written by different people. And each one could be valid.

I was impelled to write the above post after reflecting upon all those many pseudo-scientific stories of cavemen (as Rebecca Solnit put it, “the five-million-year-old suburb”) and reflecting on the difficulties we so often have in communicating with one another; see for example here (where psychologist Steven Pinker, who describes himself as a feminist, gives a list of topics that he feels are “taboo” and cannot be openly discussed among educated Americans; an example is the statement, “Do men have an innate tendency to rape?”) and here (where social critic Charles Murray, who I assume would not call himself a feminist, argues that educated Americans are too “nonjudgmental” and not willing enough to condemn others, for example by saying “that it is morally wrong for a woman to bring a baby into the world knowing that it will not have a father”).

When doing social science, we have to accept that different people have sharply different views. Awareness of multiple perspectives is to me a key step, both in understanding social behavior and also in making sense of the social science we read. I do not think that calling oneself a feminist makes someone a better person, nor do I claim that feminism represents some higher state of virtue. All I’m saying here is that feminism, beyond its political context, happens to be a perspective that can help some of us be better scientists.

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