Bibliographic record
Abstract
Human life is riddled with norms, many though not all of which are costly for individuals to adopt. Similarly, human ecological adaptation relies on costly-behaviors that often generate non-rivalrous and non-exclusionary benefits for group-members. Yet, in a dynamic world, innovations, environmental change, and information-revelation mean that what norms are beneficial for a group to adopt will inevitably change over time. However, multiple game-theoretic models studying the various mechanisms stabilizing normative behaviors have demonstrated that the stability of a norm does not depend on the benefits it confers. In turn, explanations of normative change have either relied on group-selective mechanisms to explain the presence of adaptive norms or have failed to identify conditions under which normative change occurs. We study normative change by means of costly-punishment and conflict resolution. We identify social differentiation in goals and punishment capacity as a key condition permitting normative change. While normative change that results from such social differentiation need not be group beneficial it will be beneficial to some subset of agents in the population. We additionally discuss how the intra-societal forces of normative conflict that we study might interact with group-selective forces and in turn determine the dynamics and outcomes of group-selection.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".