Individuals Adapt Their Inappropriateness Evaluation of Norm Violations Through Observation of Their Social Environment
Bibliographic record
Abstract
The extent to which violating social norms is seen as inappropriate varies between social groups. Furthermore, knowledge about how inappropriate it is to violate these social norms is unlikely to be pre-specified. Rather, individuals likely infer information about social norms from their social environments. In an experiment with American participants (N = 834), we find that observing how others evaluate the inappropriateness of a set of social norm violations leads participants to adapt their own inappropriateness evaluations to a different set of social norm violations. This suggests that inferences about the underlying local level of inappropriateness is a feature of norm learning. Our results also suggest that this process may be attuned to the type of norms being processed (General vs. COVID-19 related norms). Overall, this study shows that inference gained through observing others can be generalized and contributes to the ability to adapt and calibrate one’s evaluation of social norm violations to their local environment.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".