Message source effects on rejection and costly punishment of criticism across cultures
Bibliographic record
Abstract
Subgroups of societies evaluate information differently, leading to partisan polarization and societal rifts world-wide. Beyond mere disagreement about facts or different preferences, we identify a group-based mechanism predicting the rejection of critical messages and costly punishment of the commenter across three previously understudied and representative cultures. Our pre-registration was peer-reviewed within the Leibniz-Institute for Psychology lab-track scheme prior to data collection and, once accepted, funded. Participants (N = 2207) from China (collectivism, n = 786), Canada (individualism, n = 666), and Japan (honor, n = 755) consistently rejected criticism of their own national group that was attributed to a source from a different national group (intergroup criticism), as compared to the same criticism from within their group. These intergroup sensitivity effects were larger in China than in Canada or Japan. In Canada and Japan only, a bystander intergroup sensitivity effect emerged such that participants rejected criticism of another national group (i.e., they do not belong to) that was attributed to a source from a different national group (intergroup criticism), as compared to the same criticism from within that group. Apparently, the processes underlying this robust effect differ between cultures. We conclude that group-based message rejection contributes to societal rifts in many different cultures.
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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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".