Negative Affectivity, Political Contention, and Turnout: A Genopolitics Field Experiment
Bibliographic record
Abstract
Recent genopolitics and political psychology research suggests individuals' biological differences influence political participation. The interaction between individual differences and environments has received less attention, not least because of the confound of self‐selection into environments. To test the interaction between innate predispositions and an exogenous environmental influence, we conducted a field experiment during the 2010 California midterm elections. We randomly assigned subjects to receive a postcard mobilization treatment designed to induce an emotional response to the degree of political contention in the election. We tested the possibility that subjects who are genetically predisposed toward negative affectivity will be less likely to vote after treatment exposure. To our knowledge, this is the first field experiment in political science to measure genetic moderation of a treatment, and it suggests experimental approaches can benefit from the inclusion of genetically and other biologically informative covariates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".