Negativity Biases and Political Ideology: A Comparative Test across 17 Countries
Bibliographic record
Abstract
There is a considerable body of work across the social sciences suggesting negativity biases in human attentiveness and decision-making. Recent research suggests that individual variation in negativity biases is correlated with political ideology: persons who have stronger physiological reactions to negative stimuli, this work argues, hold more conservative attitudes. However, such results have mostly been encountered in the United States. Does the link between psychophysiological negativity biases and political ideology apply elsewhere? We answer this question with the most extensive cross-national psychophysiological study to date. Respondents across 17 countries and six continents were exposed to negative and positive televised news reports and static images. Sensors tracked participants’ skin conductance, and a survey captured their left–right political orientation. Analyses performed at three levels of aggregation—respondent-as-a-case, stimuli-as-a-case, and second-by-second time-series—fail to find strong support for the link between negativity biases and political ideology.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".