Are men more likely to be given the privilege of being identified by last name only? Thorkelson, writes another thoughtful post with a literary quality I dare not try to emulate. You should really read his post because I’m not summarizing it. I’m just replying to one little piece of it, and then explaining how one would approach this question using regression. The rest of my post also appears as a comment at Eli’s blog and I encourage readers to make comments on both sites:
Thorkelson, having read your post, I find the basic claim about men being privileged by the practice of identifying them using solely their last name to be very plausible, e.g. “Sartre” vs. “Simone de Beauvoir.” This is not something I had thought about much before either, and I agree that we should prefer that we had already observed and considered this issue without someone else bringing it to our attention.
That said, I don’t think you should chastise yourself for some skepticism. When someone makes a claim, we should ALWAYS be skeptical. Yes, we should be on the lookout for motivated skepticism. But I’d prefer that we increase skepticism of claims that justify our high status rather than decrease skepticism of claims that we benefit from unearned privilege.
As a quantitative sociologist, I think we could learn a great deal with a statistical approach. I can think of a lot of things I’d want to consider, but one of them would be to account for the prominence of the professor. More prominent professors are probably more likely to be referred to by last name only. More prominent professors are more likely to be men. What happens to the pattern when google hits and citations are accounted for? Perhaps naming is also related to behaviors/personality and behaviors/personality differ across gender.
At the risk of being pedantic I’ll give a very simple explanation of how we’d approach this problem using regression. First we identify our outcome of interest, e.g. the percentage of the time someone is referred to by last name only, first name only, or full name. Then we identify things that might predict this outcome, e.g. gender, citations, field, popularity of name, personality, etc. We run our data through the machinery of regression (important details omitted) and this gives us an equation for predicting the outcome. The “Last-Naming-Gap” is equal to the coefficient on the gender predictor. This will vary depending on which other predictors we include. If we include the right variables, the coefficient may go to zero, in which case we might say we “explained” the “last-naming-gap”? Note this would NOT disprove discrimination. For example, it is quite possible that the gender differences in citations that we are using to “explain” the last-naming-gap, are themselves partially the result of gender bias. And a bias that results in citation differences would be more obviously consequential than a bias that results in a different use of language. Anyway, thanks for the interesting post Eli.
Permutations 