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NON‐MONETARY INCENTIVES AND OPPORTUNISTIC BEHAVIOR: EVIDENCE FROM A LABORATORY PUBLIC GOOD GAME

2012· article· en· W2108509794 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.

Bibliographic record

VenueEconomic Inquiry · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIncentivePublic goodEconomicsFree ridingNash equilibriumSocial psychologyMicroeconomicsPublic goods gamePublic economicsPsychology

Abstract

fetched live from OpenAlex

This study reports data from a laboratory experiment that investigates the incentive effect of three distinct social communication schemes on free‐riding behavior. We use performance‐based approval and disapproval ratings and a linear public good game to address the above issues. The treatments vary in terms of subjects' opportunities to anonymously assign (1) only the approval ratings to other group members , (2) only the disapproval ratings to other group members, and (3) either the approval or the disapproval ratings to other group members (but not both to the same group member), after they play a standard linear public good game. Despite the Nash prediction of zero individual contribution in all three treatments, the data show that the disapproval points generate significantly higher contribution than the approval points. The treatment in which subjects could communicate either the approval or the disapproval points produces the highest level of contribution. We discuss the implications that these findings may have for efficient design of organizations . ( JEL D03, H41, C72, C92)

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.968

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 studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.123
GPT teacher head0.358
Teacher spread0.236 · 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