Perhaps the first Monkey Cage post at the Washington Post presents some numbers that show that policy-makers tend not to like the higher tech kind of poli sci (or theory) We knew this from previous TRIP reports and other studies, but still it is important to consider such stuff, especially given that quantitative work (in IR, anyway) is now about as prevalent as non-quant work.
One might be tempted to argue that we should stop or reduce quant work given that a key audience may not like it so much. My first reaction was to think about baseball. The rise of statistics to evaluate players–as depicted semi-accurately in the Moneyball book, a bit less accurately in the movie–was resisted by those in the game. That did not mean that the numbers did not capture key dynamics. Indeed, knowing the results proved to be quite helpful to those who were willing to learn or hire people who understood them.
As someone who is far more comfortable with qualitiative work but has published some quant, I tend not to be as fearful of the rise of the (quant) machines as others but also see the point that the quant work has its limits. In all things, I am a big fan of portolios and of diversity. Just as professional baseball still relies on scouts to complement the numbers, the professionals in politics need both numbers and stories, quant and qual analyses. After all, these politicians who do not like to read numbers sure as hell rely on them as they run for office via polling and market analyses. Seems to me that they should keep on relying on numbers when they govern.
So, again, the answer is not to run against the latest in political science but find ways to make it digestible to both policy folks and general publics. That this post appeared in the Monkey Cage as it starts its new life as part of the Washington Post is then especially appropriate. The MC’s aim is to do precisely that–take poli sci and present it in ways that publics and policy folks can get easily without mastering the methods behind the analyses. I do think that policy folks also will have increasingly stats-literate folks working for them, just as baseball and basketball teams hired the whiz kids who never played professionally but provided much insights with their scientific study of the games.
We can continue to think of ways to improve our dissemination of the knowledge we create. Sorry, the grant I am writing this month requires a knowledge mobilization plan so this jargon is inescapable right now. But I don’t mind thinking about such stuff–if I want public money (Canadian money in this case), I should and do accept the responsibility of trying to figure out how I will share my findings beyond the academy. This responsibility does not shape the methods I choose to study the stuff, but it does mean I will take seriously how I plan to communicate what I learn.