American Sociological Association (ASA) Style
Connecting People to Useful Information: Guidelines for Effective Data Presentations
Jane Miller, The Chicago Guide to Writing about Numbers, Chapter 12: Speaking about Numbers
FrameWorks Institute, "Translating Social Scientific Research for Non-Academic Audiences" (American Sociological Association annual meeting, August 13, 2018)
Everyday Sociology | Markets, Power, and Culture | orgtheory | scatterplot
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Hi XXXX,
No worries at all, thanks very much for taking the time to look it over. I had another op-ed idea that occurred to me after listening to the debate last night -- Sanders and Clinton were debating the merits of being more like Denmark, but my take would be that we should (and, practically speaking, could) be more like Canada, which has only small differences in its social safety net compared to the U.S., and which shares a similar type of economic system and similar set of demographics. Nevertheless, those policy differences matter greatly in terms of how people cope with unemployment, poverty, or personal crises. My book focuses on a comparison of the U.S. and Canadian social safety nets and what kind of assistance each system provides to individuals dealing with long-term unemployment. Policies regarding job retraining, health care, and family cash benefits were more generous on the Canadian side, and I could see the very real impacts those policies had on specific individuals and families struggling to deal with the loss of a job -- and, in my book, actually compare their outcomes with the ones people experienced on our side of the border. (That's not to say the Canadian system was always better, which I can get into as well.)
Would this be of any interest? I can think up other ideas if this doesn't work. Thanks for considering this!
Best,
Victor
Hi XXXX,
Hope all is well. I wanted to see if you might be interested in a piece relating to big data and its growing use to evaluate people as workers, students, and consumers. The jumping-off point is the just-published book by Cathy O'Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, which argues that the computer algorithms now in routine use by companies and governments in making decisions about hiring, promotions, sentencing, etc., serve to justify and exacerbate discrimination and inequality. While her solution is ultimately that we need to be humbler about our models and work to improve how accurately and fairly they measure merit, my research suggests that we need to challenge the underlying assumptions of this kind of analysis altogether: that meritocracy and measurement are unquestionably good things. Below are the first three grafs of my draft in progress:
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In her new book Weapons of Math Destruction, data scientist Cathy O’Neil describes how companies and governments evaluate us as consumers, workers, and students based on ever-more abundant and detailed data about our lives. In deciding whether to give us loans or jobs, promote or fire us, or target us for services or opportunities, it is now widespread practice to use computer algorithms to sift through terabytes of personal information, sort “good” people from “bad,” and ultimately arrive at supposedly impartial judgments about human potential.
O’Neil makes a convincing case that we have gone too far in relying on these models of merit, which often incorporate unstated biases, fail to truly capture slippery concepts such as teacher quality and worker motivation, and discriminate against the poor and others who can’t so easily game the metrics. Yet her argument boils down to a belief that the computer algorithms aren’t good or equitable enough — yet. They need to be refined with sounder statistical methods, she suggests, or tweaked to give preferences to people from disadvantaged backgrounds.
My own research on what I call the new technology of meritocracy, though, has convinced me that building better and fairer analytical mousetraps does not address the real problem. Even if we could perfect them, do we really want to live in a society that sorts and judges us with constant, unforgiving precision? In other words, the hypermeritocratic society we are building — in fact, have long been building, if now with shiny new hi-tech toys — has tradeoffs, particularly for the egalitarian social norms it will uproot.
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In the piece, I'd mention the history of data-driven meritocracy that O'Neil doesn't really address, but that is the important backstory to the use of big data today -- from the innovation of civil-service exams developed to select Chinese bureaucrats, to the practices of Taylorism/scientific management that measured factory-worker productivity. I also think it's critical to recognize that the word "meritocracy," coined by the British sociologist Michael Young, was originally used in a pejorative sense: the idea was that a society evaluated perfectly by talent and effort would subvert democracy and justify a new aristocracy/oligarchy, as the untalented came to see themselves as solely to blame for their low status and eventually ceded their political power and rights to the elite. I'll bring up the points I make in my book about the tradeoffs between meritocracy and egalitarianism and how the new technology of meritocracy pushes us further in an unbalanced direction. I'll also connect that larger perspective with the situation of the long-term unemployed autoworkers I interviewed, whose previous jobs represented an alternative vision of egalitarian progress: unions with their seniority practices and employment protections are arguably the antithesis of systems of merit that are meant to hire, fire, and promote based on individual skill alone. In their long bouts of joblessness, the workers I interviewed suffered from self-blame and a sense of themselves as "losers" -- which reminds us of Michael Young's warnings about meritocracy and its social costs, however laudable and worthwhile a goal it is.
Thanks for considering this!
Best,
Victor