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AbstractExternal possession refers to the phenomenon where a DP is marked as an independent argument in its clause but is interpreted as the possessor of another argument. Choctaw makes extensive use of external possession, but, puzzlingly, it comes in two distinct morphological profiles. Previous work has assumed these to be surface morphological variants, but this article shows instead that external possession in Choctaw is derived by two distinct mechanisms. One mechanism involves building a DP with an internal possessor and raising the possessor out to a higher, left‐peripheral position. The alternative mechanism involves building two unconnected DPs, one in an internal‐argument position and one in a high applicative phrase, and identifying the higher DP with the possessor θ role of the lower DP in the process of semantic composition, by a mechanism known as delayed saturation. I demonstrate that this latter mechanism can generate external possession of objects and unaccusative subjects, depending on whether there is an external argument, and is subject to a host of interpretative restrictions that the movement‐based mechanism is not. Thus I show not only that external possession can be derived by two different mechanisms but also that those mechanisms may coexist in the same language.

Affective polarization (AP)—the tendency of political partisans to view their opponents as a stigmatized "out group"—is now a major field of research. Relevant evidence in the United States derives primarily from a single source, the American National Election Studies (ANES) feeling thermometer time series. We investigate whether the design of the ANES produces overestimates of AP. We consider four mechanisms: overrepresentation of strong partisans, selection bias conditional on strong identification, priming effects of partisan content, and survey mode variation. Our analysis uses the first-ever collaboration between ANES and the General Social Survey and a novel experiment that manipulates the amount of political content in surveys. Our tests show that variation in survey mode has caused an artificial increase in the mixed-mode ANES time series, but the general increase in out-party animus is nonetheless real and not merely an artifact of selection bias or priming effects.

Early socialization research dating to the 1960s showed that children could have a partisan identity without expressing polarized evaluations of political leaders and institutions. We provide an update to the socialization literature by showing that adolescents today are just as polarized as adults. We compare our findings to a landmark 1980 socialization study and show that distrust in the opposing party has risen sharply among adolescents. We go on to show that the onset of polarization in childhood is predicted by parental influence; adolescents who share their parents' identity and whose parents are more polarized are apt to voice polarized views.

AbstractIn the literatures on public opinion and legislative behavior, there are debates over (1) how constrained preferences are and (2) whether they are captured by a single left–right spectrum or require multiple dimensions. But insufficient formalization has led scholars to equate a lack of constraint with multidimensional preferences. In this paper, we refine the concepts of constraint and dimensionality in a formal framework and describe how they translate into separate observable implications for political preferences. We use this discussion to motivate a cross-validation estimator that measures constraint and dimensionality in the context of canonical ideal point models. Using data from the public and politicians, we find that American political preferences are one-dimensional, but there is more constraint among politicians than among the mass public. Furthermore, we show that differences between politicians and the public are not explained by differences in agendas or the incentives faced by the actors.

AbstractIn text, images, merged surveys, voter files, and elsewhere, data sets are often missing important covariates, either because they are latent features of observations (such as sentiment in text) or because they are not collected (such as race in voter files). One promising approach for coping with this missing data is to find the true values of the missing covariates for a subset of the observations and then train a machine learning algorithm to predict the values of those covariates for the rest. However, plugging in these predictions without regard for prediction error renders regression analyses biased, inconsistent, and overconfident. We characterize the severity of the problem posed by prediction error, describe a procedure to avoid these inconsistencies under comparatively general assumptions, and demonstrate the performance of our estimators through simulations and a study of hostile political dialogue on the Internet. We provide software implementing our approach.

Political scientists, pundits, and citizens worry that America is entering a new period of violent partisan conflict. Provocative survey data show that a large share of Americans (between 8% and 40%) support politically motivated violence. Yet, despite media attention, political violence is rare, amounting to a little more than 1% of violent hate crimes in the United States. We reconcile these seemingly conflicting facts with four large survey experiments (n = 4,904), demonstrating that self-reported attitudes on political violence are biased upward because of respondent disengagement and survey questions that allow multiple interpretations of political violence. Addressing question wording and respondent disengagement, we find that the median of existing estimates of support for partisan violence is nearly 6 times larger than the median of our estimates (18.5% versus 2.9%). Critically, we show the prior estimates overstate support for political violence because of random responding by disengaged respondents. Respondent disengagement also inflates the relationship between support for violence and previously identified correlates by a factor of 4. Partial identification bounds imply that, under generous assumptions, support for violence among engaged and disengaged respondents is, at most, 6.86%. Finally, nearly all respondents support criminally charging suspects who commit acts of political violence. These findings suggest that, although recent acts of political violence dominate the news, they do not portend a new era of violent conflict.