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Record W2154863787 · doi:10.4284/0038-4038-2012.067

Emotions, Sanctions, and Cooperation

2013· article· en· W2154863787 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

VenueSouthern Economic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsSanctionsSocial dilemmaValence (chemistry)Punishment (psychology)DilemmaSocial psychologyEmotional valenceNorm (philosophy)PsychologySkin conductanceEconomicsMicroeconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

We use skin conductance responses and self‐reported hedonic valence to study the emotional basis of cooperation and punishment in a social dilemma. We argue that the availability of sanctions sets in motion a “virtuous emotional circle” that accompanies cooperation. Emotional reaction to free riding leads cooperators to apply sanctions. In response, and in addition to the monetary consequences of receiving sanctions, the negative emotions experienced by the free‐riders when punished lead them to increase their subsequent level of cooperation. The outcome is an increased level of cooperation that becomes a new norm. Therefore, emotions sustain both the use of altruistic punishment and cooperation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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