Conservatives Anticipate and Experience Stronger Emotional Reactions to Negative Outcomes
Bibliographic record
Abstract
The present work examined whether conservatives and liberals differ in their anticipation of their own emotional reactions to negative events. In two studies, participants imagined experiencing positive or negative outcomes in domains that do not directly concern politics. In Study 1, 190 American participants recruited online (64 male, Mage = 32 years) anticipated their emotional responses to romantic relationship outcomes. In Study 2, 97 Canadian undergraduate students (26 male, Mage = 21 years) reported on their anticipated and experienced emotional responses to academic outcomes. In both studies, more conservative participants predicted they would feel stronger negative emotions following negative outcomes than did more liberal participants. Furthermore, a longitudinal follow-up of Study 2 participants revealed that more conservative participants actually felt worse than more liberal participants after receiving a lower-than-desired exam grade. These effects remained even when controlling for the Big Five traits, prevention focus, and attachment style (Study 1), and optimism (Study 2). We discuss how the relationship between political orientation and anticipated affect likely contributes to differences between conservatives and liberals in styles of decision and policy choices.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".