Biased Beliefs About White Releasees’ Sensitivity to Social Pain
Bibliographic record
Abstract
The accurate perception of others' pain is a prerequisite to provide needed support. However, social pain perception is prone to biases. Multiple characteristics of individuals bias both physical and social pain judgments (e.g., ethnicity and facial structure). The current work extends this research to a chronically stigmatized population: released prisoners (i.e., releasees). Recognizing the large United States releasee rates and the significant role support plays in successful re-integration, we conducted four studies testing whether people have biased judgments of White male releasees' sensitivity to social pain. Compared with the noncriminally involved, people judged releasees as less sensitive to social pain in otherwise identical situations (Studies 1a-3), an effect that was mediated by perceived life hardship (Study 2). Finally, judging releasees' as relatively insensitive to social pain undermined perceivers' social support judgments (Study 3). The downstream consequences of these findings on re-integration success are discussed.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".