Pain sensitivity predicts support for moral and political views across the aisle.
Bibliographic record
Abstract
= 7,360) finding that more (vs. less) pain-sensitive liberal Americans show greater endorsement of moral foundations typically endorsed by conservatives (Studies 1a-1c), higher likelihood of voting for Trump over Biden in the 2020 presidential election, stronger support for Republican politicians, and more conservative attitudes toward contentious political issues (Studies 2a and 2b). Conservatives show the mirroring pattern. These "cross-aisle" effects of pain sensitivity are driven by heightened harm perception (Study 3). They defy lay intuitions (Study 4). They are not attributable to multicollinearity or response set. The consistent findings across studies highlight the value of deriving integrative predictions from multiple previously unconnected perspectives (social properties of pain, moral foundations theory, dyadic morality theory, principle of multiple determinants in higher mental processes). They open up novel directions for theorizing and research on why pain sensitivity predicts support for moral and political views across the aisle. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".