What is a Male Feminist? // Stats and the Social Sciences

A few years ago, a lot of people were sharing an Aziz Ansari bit about feminism. The thrust - if you don’t want to click through - is that if you think men and women deserve equal treatment, then you are a feminist, and shouldn’t try avoid the label.

It’s a good joke, but philosophically I think it is a little bit more complex. Words have multiple shades of meaning, and words with far less complex histories can be hard to define simply. Put simply, what is feminism? It can mean a wide range of views, from ‘Wages for Housework’ to ‘Lean In’ neoliberal feminism to intersectionality to TERFs to arguments about abortion and the wage gap. If someone feels that women deserve to earn equally to men, but that the wage gap is explained by revealed preferences, unequal choices of hours and profession, is he not a feminist? If a Christian believes abortion is murder, is she not a feminist?

There is undoubtedly complexity here, and I’m here to say this: I think men should not be involved in this discussion. It is women who are affected by these issues, and they who should decide what feminism is defined by, each in her own way. I think this is a reasonable claim, though not a common one, even if one does not subscribe to the identitarian “standpoint theory” epistemology that claims the impossibility of outsider knowledge

But a corollary of this is, since I can’t define feminism, I prefer to call myself pro-feminist, not a feminist. It is up to women to decide what feminism is. But I can’t call myself something I can’t define.

Put another way: “I don’t know what feminism is, but whatever it is, I’m for it.”

Data Science may be a sexy term for statistics, but statistics is more sexy than it’s given credit for. Any social science, under the Popperian paradigm, must make predictions: diagnosing a structure means nothing if it has no predictive capacity. (Or else it’s humanities, kind of the way some historians want to go, and the way psychoanalysis has gone completely). Are, say, political scientists any better than monkeys throwing darts? That’s why they need statistics. So both data science and all of the social sciences can be defined as applied statistics (with the balance just varying between domain knowledge vs stats knowledge, though any long-term DS project does involve getting deep into the domain knowledge). Where it gets weird is where DS models and makes predictions without an explanatory model, such as the case of NLP. Statistical relationships working by themselves, not being supported by any deep idea of how things fundamentally are supposed to work. The next word is “meeting”, that green lump is a tree, just based on probability. This was the subject of a debate between Google’s Peter Norvig and Noam Chomsky, the former sums it up in a fascinating, deep-ranging blog post over here.