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AbstractDemocrats and Republicans clearly dislike one another. Yet, scholars debate whether policy disagreement or partisan identity, per se, drives interparty animus. Past studies suggest the relationship between partisanship and interpersonal affect is spurious, driven by inferred policy preferences. We argue, instead, that policy preferences signal partisan identity when the parties' stances on an issue are well‐known. Using a nationally representative survey and four preregistered experiments, we disentangle the effects of policy disagreement and partisan identity on interpersonal affect. Our findings suggest that partisan identity is the principal mechanism of affective polarization, and that policy preferences factor into affective polarization largely by signaling partisan identity. However, our results also affirm that policy disagreement in itself drives interpersonal affect. This provides evidence that partisanship reflects an emotional attachment to a political party, not merely a running tally of rational considerations.

AbstractThe fact checking field has grown tremendously in the past decade, as has academic interest in the practice, with dozens of studies testing the effectiveness of corrections. However, research on fact checking is not yet optimised to help fact checkers address the global challenges of mis‐ and disinformation. In this paper, we review the literature on fact checking's effects and identify two key gaps. First, we discuss the limited diversity and external validity of existing studies, which have overwhelmingly been conducted in Western countries and under artificial, experimental conditions. Second, we argue that research has narrowly focussed on the short‐term, corrective effects of individual fact checks, largely ignoring the multiple ways fact checkers conceive of their impact. Thus, research has overlooked the cultural and systemic changes that fact checkers pursue. We conclude by highlighting opportunities for further research and for improving communication between academics and fact checkers.

Political interest is a key predictor of likelihood to vote. We argue that the political interest–vote intention relationship can be explained by well-established theories that predict behavior across domains (e.g., theory of reasoned action, theory of planned behavior). Relying on the integrated behavioral model, we propose a core mediation model with vote likelihood (i.e., behavioral intention) as the dependent variable. Two types of media use (conservative and liberal–moderate) are then assessed in relation to the core model. We explore the ways in which our results contribute to theory and outline a research agenda.

Abstract
Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.