Dispositional Traits and Susceptibility to Political Anxiety
Bibliographic record
Abstract
Who experiences political anxiety and how do these unique experiences influence engagement with and use of political information? Prior work on anxiety and information engagement has treated everyone as equally susceptible to experiencing situational anxiety over politics. In this paper, I use a survey, cognitive behavioral task, and two experiments (one randomized, one selection) to demonstrate that individual-level psychological traits (e.g. trait anxiety) predispose certain people to experience situational anxiety about politics. These unique emotional experiences, because of individual-level traits, lead people to engage with more political information, particularly threatening political information. As a result, individuals use information they have gathered in different ways to engage with politics more directly. In sum, people whose psychological traits predispose them to experience anxiety about politics consume a greater amount of threatening political information and a greater desire to use this information to engage in the political process.Utilizing a simulated information news board, I test this series of links and find that individual traits affect the propensity to experience political anxiety via attentional biases and this propensity influences the type of political information with which individuals engage. People high in trait anxiety show attentional biases towards anxiety-inducing content, the first study in political science to show trait anxious people show cognitive differences from people who are not trait anxious when it comes to politics. People high in trait anxiety also seek out a larger amount of threatening political information when experiencing anxiety over politics. Once individuals seek out a higher amount of threatening political information, they express more desire to engage in politics. This work highlights the importance of incorporating dispositional traits in research on emotions and politics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".