Human and nonhuman norms: a dimensional framework
Bibliographic record
Abstract
Human communities teem with a variety of social norms. In order to change unjust and harmful social norms, it is crucial to identify the psychological processes that give rise to them. Most researchers take it for granted that social norms are uniquely human. By contrast, we approach this matter from a comparative perspective, leveraging recent research on animal social behaviour. While there is currently only suggestive evidence for norms in nonhuman communities, we argue that human social norms are likely produced by a wide range of mechanisms, many of which we share with nonhuman animals. Approaching this variability from a comparative perspective can help norm researchers expand and reframe the range of hypotheses they test when attempting to understand the causes of socially normative behaviours in humans. First, we diagnose some of the theoretical obstacles to developing a comparative science of social norms, and offer a few basic constructs and distinctions to help norm researchers overcome these obstacles. Then we develop a six-dimensional model of the psychological and social factors that contribute to variability in both human and potential nonhuman norms. This article is part of the theme issue 'Social norm change: drivers and consequences'.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".