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Record W1885334880 · doi:10.1093/jleo/ewu019

Sustaining Group Reputation

2015· article· en· W1885334880 on OpenAlex
Erik O. Kimbrough, Jared Rubin

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.

Bibliographic record

VenueThe Journal of Law Economics and Organization · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReputationUnobservableTemptationPunishment (psychology)Group (periodic table)Social psychologyEnforcementMatching (statistics)PsychologyMicroeconomicsEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

When individuals trade with strangers, there is a temptation to renege on agreements. If repeated interaction or exogenous enforcement is unavailable, societies often solve this problem via institutions that rely on group, rather than individual, reputation. Groups can employ two mechanisms to uphold reputation that are unavailable to individuals: information sharing and in-group punishment. We design a laboratory experiment to distinguish the roles of these mechanisms when individual reputations are unobservable. Subjects are split into groups and play a trust game with random re-matching, where only the group identity of one’s partner is known. Treatments differ by whether information about group members’ transactions is shared and whether in-group punishment is possible. We find that information sharing encourages path dependence via group reputation: good (bad) behavior results in greater (fewer) gains from exchange in the future. However, the mere threat of in-group punishment is enough to discourage bad behavior. (JEL C9, D02, D7)

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.036
GPT teacher head0.296
Teacher spread0.260 · 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