Drinking Too Much and Feeling Bad About It? How Group Identification Moderates Experiences of Guilt and Shame Following Norm Transgression
Bibliographic record
Abstract
The role of reference group norms in self-regulation was examined from the perspective of transgressions. Results from four studies suggest that following the transgression of a reference group's norms, individuals who strongly identify with their group report more intense feelings of guilt, an emotion reflecting an inference that "bad" behaviors are perceived as the cause of the transgression. Conversely, weakly identified individuals reported more intense feelings of shame, an emotion reflecting an inference that "bad" characteristics of the person are perceived as the cause of the transgression. The studies also explored the differential relevance of the reference groups when assessing transgressive behaviors, the counterfactual thoughts individuals have about possible causes for the transgressions, and the motivational outcomes of guilt and shame using behavioral data. Results of the studies offer insights into self-regulation, maintenance of group norms, and offer implications for alcohol consumption interventions, such as social marketing campaigns.
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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 | 0.008 |
| 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 | 0.002 |
| 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".