Criminality labelling influences reactions to others’ pain
Bibliographic record
Abstract
Disparities in healthcare for underrepresented and stigmatized groups are well documented. Current understanding is that these inequalities arise, at least in part, from psychosocial factors such as stereotypes and in-group/out-group categorization. Pain management, perhaps because of the subjective nature of pain, is one area of research that has spearheaded these efforts. We investigated how observers react to the pain of individuals labelled as criminals. Face models expressing pain of different levels of intensity were portrayed as having committed a crime or not (control group). A sample of n = 327 college students were asked to estimate the intensity of the pain expressed by face models as well as their willingness to help them. Trait empathy was also measured. Data was analyzed using regression, mediation and moderation analyses. We show for the first time that observers were less willing to help individuals with a criminal history. Moreover, a moderation effect was observed whereby empathic participants were more willing to help control face models compared to less empathic participants. However, criminality history did not influence participant's pain estimation. We conclude that negative stereotypes associated with criminality can reduce willingness to help individuals in pain even when pain signals are accurately perceived.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".