Bibliographic record
Abstract
This paper analyzes how private decisions and public policies are shaped by personal and societal preferences ("values"), material or other explicit incentives ("laws") and social sanctions or rewards ("norms").It first examines how honor, stigma and social norms arise from individuals' behaviors and inferences, and how they interact with material incentives.It then characterizes optimal incentive-setting in the presence of norms, deriving in particular appropriately modified versions of Pigou and Ramsey taxation.Incorporating agents' imperfect knowledge of the distribution of preferences opens up to analysis several new questions.The first is social psychologists' practice of "norms-based interventions", namely campaigns and messages that seek to alter people's perceptions of what constitutes "normal" behavior or values among their peers.The model makes clear how such interventions operate but also how their effectiveness is limited by a credibility problem, particularly when the descriptive and prescriptive norms conflict.The next main question is the expressive role of law.The choices of legislators and other principals naturally reflect their knowledge of societal preferences, and these same "community standards" are also what shapes social judgments and moral sentiments.Setting law thus means both imposing material incentives and sending a message about society's values, and hence about the norms that different behaviors are likely to encounter.The analysis, combining an informed principal with individually signaling agents, makes precise the notion of expressive law, determining in particular when a weakening or a strengthening of incentives is called for.Pushing further this logic, the paper also sheds light on why societies are often resistant to the message of economists, as well as on why they renounce certain policies, such as "cruel and unusual" punishments, irrespective of effectiveness considerations, in order to express their being "civilized".
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".