Shame and anger differentially predict disidentification between collectivistic and individualistic societies
Bibliographic record
Abstract
In the present research we tested the differential effects of anger versus shame as emotional predictors of ingroup disidentification in one rather collectivistic (Japan) and two rather individualistic societies (Germany, Canada). We tested the idea that individuals cope with socially undesired emotions by disidentifying from their group. Specifically, we predicted that after a group conflict, anger, an undesired emotion in Japan, would elicit disidentification in Japan, whereas shame, an undesired emotion in Canada and Germany, would elicit disidentification in Germany and Canada. Study 1 (N = 378) found that anger, but not shame, was related to disidentification in Japan, whereas shame, but not anger, was related to disidentification in Canada and Germany. Study 2 (N = 171) shows that, after group conflict, Japanese disidentified more when imagining to feel angry, whereas Germans disidentified more when imagining to feel ashamed. Implications for these findings are discussed.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".