It shouldn’t be a surprise that my postings have dropped off recently, given the demands of my work schedule, but I wanted to write a special pre-election post to discuss a few trends in people writing about campaigns.
A recent article by Thomas Gilbert and Andrew Loveridge questions the increasing trend to try and predict the upcoming election. While quantitative analysts like Nate Silver may make better predictions than other pundits, Gilbert and Loveridge argue the very act of making predictions is the wrong goal for journalism:
“The real problem with our media wasn’t that it was bad at predicting elections (although it was)—it’s that it spends so much time on predicting elections at all, as opposed to moderating and shaping a national debate on what is at stake at the ballot box. Statisticians like Silver have helped eliminate bias when it comes to election prognostication, but there hasn’t been a similar commitment to eliminating the bias of spurious political narratives peddled by major media outlets. This leaves data journalism in the unfortunate position of helping to predict our electoral choices without evaluating their significance and pointing to alternatives.” (emphasis added)
This criticism of Silver is not new or surprising. Silver himself told New York Magazine “we’re not trying to do advocacy here. We’re trying to just do analysis. We’re not trying to sway public opinion on anything except trying to make them more numerate.” When Silver reintroduced FiveThirtyEight under ESPN, he provided the following template for mapping different forms of journalism:
You will notice that “advocacy” is not a part of Silver’s 2×2 table. It is out of bounds, completely separate from journalism. Gilbert and Loveridge argue that this is a problem with “big data.” While “big data” is an increasingly common boogeyman, it is not the quantitative approach of data journalism that causes a problem. Traditional punditry isn’t their answer. Neither is ethnography or some other rigorous qualitative approach to studying voter behavior. Instead, Gilbert and Loveridge are part of a long line of scholars who argue against the goals of most American political journalism:
“Nate Silver should not be lumped together with Bill O’Reilly or Glenn Beck as an enemy of civic engagement; he lives and operates in a social reality very close to our own. But he does have one thing in common with them: persuading people into perceiving politics through the aesthetic coherence of his models at the expense of their own political imaginations. This is the danger inherent in Big Data qua ideology, rather than a tool in the service of inquiry.”
I would argue that Nate Silver’s 2×2 table needs a third axis: analytic vs. advocacy. Silver and many other data-intensive analytic approaches are dedicated first and foremost to trying to understand what people do and why they do it. Prediction is on an extreme end of the analytic spectrum, because it assumes prior behavior will help us to understand future behavior to some degree. But there is another extreme to data journalism and many forms of academic scholarship: it is non-judgmental. Nate Silver has political preferences, but he tries to keep them out of his analyses.
The advocacy axis has grown by leaps and bounds in an era of digital publication. There are plenty of websites you could go to that will outline the stakes of an election, who to vote for, and the dire consequences if you even think about voting the wrong way. Some of these sites provide valuable inquiry and allow for a broader range of political opinions than we would see in any form of journalism, whether it is data journalism or traditional punditry. Of course, there are a large number of problems with these sites, particularly if people rely on them as their main source of information.
Big data may be new, but picking on journalists for being too far on the “analytic/descriptive” axis instead of the “advocacy/take a stand” axis is not. Progressive academics have critiqued mainstream media for being “empty” or “inadequate” without some form of advocacy since the Vietnam War, when critical scholars wanted news organizations to take an active anti-war stance instead of describing policymakers’ positions on war. Gilbert and Loveridge’s ideal would seem to be a combination of advocacy, rigor and either quantitative or mixed methods. It’s certainly an intriguing box. It seems preferable to other advocacy-based political writing, which is ad hoc and full of ad hominem attacks.
Unfortunately, the box seems impossible to fill. Trying to explain what people do and why they do it is the main goal for an analytic writer. Whether the writing is quantitative or qualitative, descriptive or causal, the goal is to understand what others are doing instead of judging those decisions. In most forms of advocacy writing, the goal is ultimately to sway people to do something else. These pieces can have a comparative advantage in introducing new ideas to a political debate, something that more analytic political writing is particularly bad at. More thoughtful forms of political advocacy that do not quickly devolve in to tribal allegiances could be a tremendously valuable part of political participation. However, it doesn’t make sense to try and push data journalism (or social scientific big data) into this advocacy box, because the goals and strengths do not overlap.