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Record W2395905685 · doi:10.1111/jeea.12152

NORMS MAKE PREFERENCES SOCIAL

2015· article· en· W2395905685 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the European Economic Association · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaUniversiteit MaastrichtEuropean Commission
KeywordsStylized factUltimatum gameProsocial behaviorDictator gameDictatorPublic goodMicroeconomicsRobustness (evolution)SelfishnessEconomicsSocial preferencesNorm (philosophy)Social psychologyPublic goods gameAltruism (biology)Interpretation (philosophy)Positive economicsPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

We explore the idea that prosocial behavior in experimental games is driven by social norms imported into the laboratory. Under this view, differences in behavior across subjects is driven by heterogeneity in sensitivity to social norms. We introduce an incentivized method of eliciting individual norm-sensitivity, and we show how it relates to play in public goods, trust, dictator, and ultimatum games. We show how our observations can be rationalized in a stylized model of norm-dependent preferences under reasonable assumptions about the nature of social norms. Then we directly elicit norms in these games to test the robustness of our interpretation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.309
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it