(Anti-)egalitarianism differentially predicts empathy for members of advantaged versus disadvantaged groups.
Bibliographic record
Abstract
We explore the relationship between group-based egalitarianism and empathy for members of advantaged groups (e.g., corporate executives; state officials) versus disadvantaged groups (e.g., blue-collar workers; schoolteachers) subjected to harmful actions, events, or policies. Whereas previous research suggests that anti-egalitarians (vs. egalitarians) dispositionally exhibit less empathy for others, we propose that this relationship depends on the target's position in the social hierarchy. We examined this question across eight studies (N = 3,154) conducted in the U.S. and the U.K., including online and in-person experiments and examining attitudinal and behavioral outcomes. We observed that (anti-)egalitarianism negatively predicted empathy for members of disadvantaged groups subjected to harmful situations, but positively predicted empathy for members of advantaged groups. This pattern held regardless of perceivers' own membership in advantaged or disadvantaged groups (i.e., perceiver gender, race, or SES). (Anti-)egalitarianism's differential effects on empathy for advantaged versus disadvantaged targets were attributable in part to differences in perceived degree of harm incurred (beyond roles for perceived value conflict and perceived deservingness): Egalitarians perceived the same action as more harmful than anti-egalitarians when it occurred to a disadvantaged target but less harmful than anti-egalitarians when it occurred to an advantaged target. We also explored how these patterns informed individuals' downstream policy attitudes and policy-relevant behavior (e.g., willingness to sign a petition). Our findings enrich understanding of (anti-)egalitarianism by testing competing perspectives on the link between (anti-)egalitarianism and empathy, and by demonstrating when and why individuals' preferences for social equality (vs. hierarchy) lead them to extend versus withhold empathy. (PsycINFO Database Record
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".