Social Class and Social Pain: Target SES Biases Judgments of Pain and Support for White Target Individuals
Bibliographic record
Abstract
Social pain, defined as distress caused by negative interpersonal experiences (e.g., ostracism, mistreatment), is detrimental to health. Yet, it is unclear how social class might shape judgments of the social pains of low-socioeconomic status (SES) and high-SES individuals. Five studies tested competing toughness and empathy predictions for SES’s effect on social pain judgments. Consistent with an empathy account, in all studies ( N cumulative = 1,046), low-SES White targets were judged more sensitive to social pain than high-SES White targets. Further, empathy mediated these effects, such that participants felt greater empathy and expected more social pain for low-SES targets relative to high-SES targets. Social pain judgments also informed judgments of social support needs, as low-SES targets were presumed to need more coping resources to manage hurtful events than high-SES targets. The current findings provide initial evidence that empathic concern for low-SES White individuals sensitizes social pain judgments and increases expected support needs for lower class White individuals.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".