Despite its growing importance in social scientific topics, the quantitative measurement of polarization has lagged behind its conceptual development. Political and social polarization are group-based phenomena characterized by intergroup heterogeneity and intragroup homogeneity, but existing measures capture only one of these features or make it difficult to compare across cases or over time. To bring the concept and measurement of polarization into closer alignment, I introduce the cluster-polarization coefficient (CPC), a measure of multimodality that allows scholars to incorporate multiple variables and compare across contexts with varying numbers of parties or social groups. Simulation exercises and three applications to ideological and affective polarization demonstrate that the CPC returns more substantively sensible results than other popular measures. An open-source software package implements the measure.
Isaac D. Mehlhaff, Timothy J. Ryan, Marc Hetherington, and Michael MacKuen. “Where Motivated Reasoning Withers and Looms Large: Fear and Partisan Reactions to the Covid-19 Pandemic.” Revise and Resubmit, American Journal of Political Science. [Working Paper] [Supplementary Information] [Monkey Cage]
Contemporary American politics has been largely characterized by hyper-partisanship and polarization, with partisan motivated reasoning a thematic concern. Theories of emotions in politics suggest that anxiety might interrupt partisan heuristics and encourage citizens to reason more evenhandedly—but in what domains and to what extent? We use original panel data to assess how anxiety about becoming seriously ill from Covid-19 interacted with partisan attachments to shape political judgment during the Covid-19 pandemic. The structure of our data allows us to assess large-scale implications of politically relevant emotions in ways that so far have not been possible. We find large effects on policy attitudes: Republicans who were afraid of getting sick rejected signals from co-partisan leaders by supporting mask mandates and the like. Effects on vote choice were muted in comparison, but, in a race as close as the 2020 presidential election, were potentially large enough to have been pivotal.
Marc J. Hetherington, Isaac D. Mehlhaff, and Caroline Marie Lancaster. “Worldview Politics in the United States and Great Britain.” Under Review. (Paper available upon request.)
Scholars consistently find that most people do not think about politics in ideological terms. What, then, underlies the broad outlines of how citizens think about politics? Building on past work on authoritarianism, we implicate a driving force behind individuals’ political attitudes in their philosophy about life—their “worldview.” We posit that people use beliefs developed from their interactions with their everyday world to inform their affinities and preferences when analogous matters enter the political realm. We theorize and empirically validate a multidimensional measure of worldview in the United States and United Kingdom. Our measure consists of four related, but distinct, dimensions: authority, community, competitiveness, and incrementalism. We show that each is related to support for party identification, ideology, and a range of political attitudes in sensible ways.
Robert C. Luskin, Daron R. Shaw, Marc J. Hetherington, and Isaac D. Mehlhaff. “Signed Forecast Errors in Pre-Election Media Polls: A Mainly Political Story.” Under Review. (Paper available upon request.)
This paper examines the signed forecast errors made by state-level pre-election media polls from 1990 through 2020. On average, these polls under-forecast the Republican vote, but only slightly, and with widely varying signs and magnitudes—partly as a function of how the poll is conducted, but more as a function of the campaign and its political context. Underlying many of these political influences is a pattern of partisan straying (at the time of the poll) and subsequent homecoming (by Election Day), in turn affected by both campaign advantages, like outspending one’s opponent, and environmental ones, like the electorate’s being mostly of one’s party. Breaking the signed forecast error apart, we estimate a two-equation SUR model expressing the Republican’s poll share and vote share as functions of campaign and environmental advantages, polling details, and the difference between two “eras,” the more recent marked by heightened politicization and partisanship and an inversion, among whites, of the parties’ previous alignment by education.
Samuel Schmitt, Isaac D. Mehlhaff, and Emily Cottle Ommundsen. “Integrating Classroom and Community with Undergraduate Civically Engaged Research.” Under Review. (Paper available upon request.)
Social scientists are frequently interested in how individuals discuss controversial issues, but annotating text or speech data on a large scale is expensive and time-consuming. Argument mining, a subfield of natural language processing, uses computational models to extract argumentative structure and reasoning from text. I introduce the field of argument mining into political science and show how a deep learning approach, in particular, holds promise for understanding argumentation in real-world political discussion, even on challenging tasks like detecting argument quality and distinguishing between emotion- and fact-based arguments. Across nine tasks, deep learning models afford substantial improvements over more common feature extraction strategies, and they achieve state-of-the-art results on three tasks with previously set metrics. The strong results attained by the classifiers developed in this paper suggest that argument mining offers significant potential for facilitating the study of persuasion and interpersonal discussion.
Prevailing theories of public opinion and political psychology hold that human reasoning is biased and lazy, which suggests it is ill-suited to help ordinary citizens engage meaningfully with politics. In contrast, I contend that the biased and lazy nature of reasoning is precisely what gives citizens the tools to think through political issues and update their attitudes in response to argumentative exchanges. To test these hypotheses, I train a series of deep neural networks to classify textual inputs on several characteristics of discussion and argumentation. I use these classifiers to annotate over one million comments from the Reddit social media platform and show that attitude change is substantially more likely to result from argumentative exchanges rather than more contemplative ones. Results suggest that under the right conditions, humans can be quite skilled political reasoners.
An antagonistic political culture has long been thought to pose a threat to liberal democracy. More recently, many scholars have proposed a link between political polarization and democratic breakdown, yet causal evidence for this prominent theory remains thin. I present the first broadly comparative analysis of the relationship between mass polarization and democratic backsliding, the modal form of autocratic reversion in the post-third wave era. Panel estimates of ideological and affective polarization from as many as ninety countries and forty-nine years indicate that both ideological and affective polarization exert negligible causal effects on levels of electoral and liberal democracy. To the contrary, results suggest that democratic decline may actually foment mass polarization. Despite widespread concern over the fate of democracy in polarized polities, comparative evidence since the start of the third wave suggests that mass polarization itself poses little threat to democratic regimes.
Isaac D. Mehlhaff. “Subverting Solidarity: The Role of American Organized Labor in Pursuing United States Foreign Policy Objectives in Chile, 1961-1973.” The Burkhardt Review 1, no. 2 (2018), 24-40. [Article]