Group‐based guilt and shame in the context of intergroup conflict: The role of beliefs and meta‐beliefs about group malleability
Bibliographic record
Abstract
Abstract Group‐based guilt and shame are part of a wide range of moral emotions in intergroup conflicts. These emotions can potentially motivate group members to make compromises in order to promote conflict resolution, and increase support for reparations and apologies following moral transgressions committed by the in‐group. Thus, it is important to understand how to induce these emotions and the mechanisms for their effects. In the present paper, we examined the mechanisms underlying group‐based guilt and shame in four studies. Across the first three studies, conducted in the context of the Israeli‐Palestinian conflict, we found that group‐based guilt was mostly predicted by individuals’ implicit theories about groups (ITG). Specifically, we found that the more participants believed that groups are malleable, the more they experienced group‐based guilt. Group‐based shame, however, was found to be dependent upon individuals’ perception of other people’s perceptions about the malleability of groups (i.e., meta‐ITG), as the perceived damage to one’s in‐group image is a major component in experiencing shame. In Study 4, conducted in the context of gender relations, we differentiated between the two components of shame, that is, moral and image shame. As predicted, while group‐based guilt and moral shame showed similar patterns of results, meta‐ITG had a moderating effect on the association between ITG and group‐based image shame. The theoretical and practical implications of the findings are discussed in relation to promoting intergroup conflict resolution and reconciliation.
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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 | 0.005 | 0.022 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".