In my long running quest to make xkcd’s parody real, yesterday on Twitter and Facebook I asked, sociologists to rank the generalist journals from best to worst. Practically, I circulated a wiki survey via AllOurIdeas (you can still take it here, but I won’t update these rankings right away) to that effect, stating the question as “Which generalist sociology journal is better? [Imagine evaluating a CV for a job.]” So far, users have cast 5870 votes across 189 unique sessions (though I haven’t dug into the details beyond that, so it’s possible that Fabio cast most of the votes). My goal here, as the prompt indicates, was to cue the context of glancing at someone’s CV and seeing one of the listed journals, though who knows how it was actually interpreted. Here are the rankings as of Thursday, 9/28, 8:40am:
The following is a guest post by Cléo Chassonnery-Zaïgouche.
Millicent Garrett Fawcett will be honored with the first-ever statue of a woman in Parliament Square, Westminster, London. And the first created by a woman artist. Following recent additions—Lloyd George and Mandela in 2007, Gandhi in 2014—the statue will stand among eleven great men in a square that symbolizes British democracy. The announcement follows a heated debate over which women’s rights activist should be honored: suffragist Fawcett, or suffragette Emmeline Pankhurst? The debate over which individual to distinguish echoes the debates over the efficacy of political strategies, opposing the moderate constitutionalist suffragist movement led by Fawcett to Pankhurst’s “militant” strategies of the suffragettes—setting fire to public and private property, chaining themselves to railways, disrupting political events, and destroying paintings at the National Gallery.
Many agree that having a statue of a woman in Parliament Square is a good thing, backed by a long list of studies on the effect of the low representation of women in public space and in history. But the history of women’s fights for their rights, back in the 1880s as now, is crowded with women with different styles, intellectual journeys and political commitments. We definitely need more heroines, but what does the choice of just one tell us?
The following is a guest post by Shreeharsh Kelkar.
On this blog, and elsewhere, Greggor Mattson, Phil Cohen, and many others have written thoughtful, principled critiques of the recent “gaydar” study by Yilun Wang and Michal Kosinski (henceforth I’ll refer to the authors as just Kosinsky since he seems to be the primary spokesperson). I fully agree with them: the study both does too much and too little. It purports to “advance our understanding of the origins of sexual orientation and the limits of human perception” (!) through a paltry analysis of 35,326 images (and responses to these images by anonymous humans on Amazon Mechanical Turk). And it aims to vaguely warn us about rapacious corporations using machine learning programs to surreptitiously identify sexual orientation but the warning seems almost like an afterthought: if the authors were really serious about this warning, they could have dug deeper with a feasibility study rather than sliding quickly into thinking about the biological underpinnings of sexuality.
As someone who follows and studies the history of artificial intelligence (as I do), there are some striking parallels between the argument between Kosinsky and his critics, and early controversies over AI in the 1960s-80s, and I will also argue, some lessons to be learnt.
News coverage about the Graham-Cassidy bill has been inescapable in recent weeks. This news coverage has primarily focused on comparing the Graham-Cassidy bill with the Patient Protection and Affordable Care Act (ACA) in terms of essential benefits and caps on coverage. However, there has been some confusion over how this bill will affect coverage for mental health and substance use disorder treatment. Furthermore, there has been little consideration of the potentially broader effects of this bill, particularly regarding crime.
This blog post aims to: a.) Demystify mental health parity laws and explain the relationship between mental health parity laws and essential benefits coverage in the Graham-Cassidy bill; and b.) Encourage a discussion regarding a potential relationship between mental health and substance use parity laws, treatment for substance use and mental health disorders, and crime. If enacted, how might the Graham-Cassidy bill affect crime? Research on the relationship between treatment for substance use disorders and mental illness and crime would arguably suggest that eliminating full parity of mental health coverage would increase crime.
In one of the questions he asked, only 47% of students favored, “an open learning environment where students are exposed to all types of speech and viewpoints, even if it means allowing speech that is offensive or biased against certain groups of people.” In contrast, 53% favored speech restrictions to, “create a positive learning environment.”
This is a huge swing from last year when Gallup asked the same question. They found that only 22% favored speech restrictions.
This 30-point shift could be because attitudes changed rapidly. Villasenor’s study was immediately after Charlottesville, for example, and students might be more primed to think about Nazi’s marching on their campus.
It could also be because of differences in survey methods. Surveying college students is really hard.
My department has run a number of workshops (organized by grad students) on “teaching about race.” They asked me to speak about what the rules are about what we can and cannot say in the classroom. I was pretty sure I knew the “rules” but asked our Provost for the official statement. Interestingly, there was none, but the question was referred to the Legal department. After a delay, Legal Affairs sent back an email citing Wisconsin state statutes and linking to some policy statements. I’ve pasted the original correspondence below.* First a student and I translated the legalese into English bullet points. Then I wrote an essay about how to think about the authority and ethical responsibility in teaching controversial topics. This was recirculated this fall and as I’ve gotten positive feedback about this, I decided to post it here, with a few more edits, in case it is helpful. There’s always more to say, and legitimate disagreement about how to handle some things. Feel free to use the comments to expand on these points. Continue reading “exercising judgment in teaching about controversial issues”
Until the 2016 election, it was very easy for Americans to convince ourselves that racial inequality was getting better. Look, a Black president! What could be a better sign of improvement in race relations? Of course, as sociologists like Eduardo Bonilla Silva have long argued, “Obama’s America” offered a promise of colorblindness, not a reality of racial equality. Three recent data points are worth keeping in mind when thinking about the (lack of) progress on racial inequality in the past 30 years – even before White Supremacy came back to the front page, and Trump entered the Oval Office.
There are ample reasons to be skeptical of recent headlines announcing that “AI [Artificial Intelligence] Can Tell If You’re Gay,” summaries of a pre-print study by Yilun Wang and Michal Kosinski of Stanford University’s School of Business. Their goal is to “advance our understanding of the origins of sexual orientation and the limits of human perception” (p.1). For the first they fail miserably but I concur with the second, though the perceptions that are limited, in this case, are the researchers’ own. In this post I review the underpinnings of this research that render it much less insightful than the researchers claim, the problems of journalistic reporting that compound these problems, and the stunning tone-deafness of Kosinski’s defense of his ethics.
Marion Fourcade and Kieran Healy (2017) introduced the wonderful concept of ubercapital, “a form of capital flowing from [individuals’] positions as measured by various digital scoring and ranking methods” with consequences for stratification. Two new papers provide clear (and terrifying) analysis of how ubercapital works in hiring and law enforcement.
As questions about the future of affirmative action once again rise to the surface of the water cooler talk on the internet, there has been a concomitant rise in excoriations of legacy admissions as “affirmative action for white people” and “the real affirmative action.” Most recently, a freelance writer tweeted some of the results of The Harvard Crimson’s survey of the entering freshman class of 2021, focusing on a figure representing the percentages of students in the entering class who have various familial ties to alumni of Harvard College. Based on the survey data, Murphy reported that the percentage of legacies in the entering class is an eye-popping 41.2%. Shocking, right? (or perhaps not, depending on how cynical you are about elite higher ed).
But let’s take a step back, because there’s more than meets the eye with this figure and the accompanying tweet(s).