The role of group-based moral pride in regulating guilt and shame for intergroup transgressions
Bibliographic record
Abstract
When people from advantaged groups are confronted with information about their group’s immoral actions, they tend to respond with different group-based self-conscious emotions. Group-based guilt and moral shame are associated with intergroup reparation intentions, whereas image shame for the ingroup’s tarnished reputation leads members to avoid reconciliation. We introduce and test a model for how group-based moral pride regulates these different reactions to the ingroup’s transgressions. Across Studies 1-2, Canadians high on authentic group-based moral pride (AGMP), which entails genuine and grounded feelings of moral accomplishment, experienced higher levels of guilt and moral shame for Canada’s genocide of Indigenous people. Conversely, people high on hubristic group-based moral pride (HGMP), an inflated sense of moral superiority, experienced higher levels of image shame. Group-based moral shame accounted for a positive indirect association between AGMP and support for reparations, whereas image shame accounted for a positive indirect association between HGMP and avoidance intentions. In Study 3, we show that these divergent associations between each facet of group-based moral pride and guilt/moral shame are explained by defensive appraisals of an ingroup moral threat. In Studies 4-5, manipulated feelings of AGMP causally increased group-based guilt and moral shame, and these effects were found among both non-Indigenous Canadians (Study 4) and White Americans (Study 5). Group-based moral shame, in turn, mediated the effects of manipulated AGMP on increased support for reparations and decreased support for avoidance. These results contribute to a theoretical understanding of how self-conscious emotions regulate one another to influence intergroup relations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".