Egalitarian norms can deflate identity-bias link in real-life groups
Bibliographic record
Abstract
Social identity theory posits that group membership influences individual behavior by fostering a sense of belonging and promoting normative conformity within groups. While much research has shown a link between ingroup identification and ingroup bias, the role of ingroup norms in moderating this association remains less explored. Specifically, how varying norms (egalitarianism vs. favoritism) affect bias in individuals with high ingroup identification requires further investigation. To address this gap, we examined whether ingroup norms alter the strength of the identification-bias relationship in two studies (N = 322). We investigated how non-WEIRD real-life group members' ingroup bias was driven by their identification levels and perceived ingroup norm in Study 1 with a correlational design, and we experimentally manipulated ingroup norms in a simulated group discussion in Study 2. Both studies demonstrated that under a favoritism norm, participants with high ingroup identification showed greater ingroup bias, whereas this bias was deflated under an egalitarianism norm. However, contrary to our hypothesis, we did not find evidence that participants with high ingroup identification showed lower ingroup bias under the egalitarianism norm. We discuss these findings and suggest that fostering egalitarian norms within groups may reduce ingroup bias and discrimination, offering insight for interventions aimed at promoting intergroup harmony.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".