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DO WEALTH DIFFERENCES AFFECT FAIRNESS CONSIDERATIONS?*

2006· article· en· W2077651125 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

VenueInternational Economic Review · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversité de MontréalCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsAffect (linguistics)OddsUltimatum gameReinforcement learningEconomicsPsychologyMicroeconomicsReinforcementSocial psychologyEstimationComputer scienceLogistic regressionArtificial intelligence

Abstract

fetched live from OpenAlex

The influence of relative wealth on fairness considerations is analyzed in an ultimatum game experiment in which participants receive large and widely unequal initial endowments. Subjects initially demonstrate a concern for fairness. With time however, behavior becomes at odds with both subgame perfection and fairness. Evidence of learning is detected for both proposers and receivers in the estimation of a structural reinforcement learning model. The estimation results suggest that, guided by foregone best responses and an acquired sense of deservingness, rich subjects become more selfish, whereas poor subjects, influenced only by their own experience, learn to tolerate this behavior.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.063
GPT teacher head0.388
Teacher spread0.324 · 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