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Record W4387431662 · doi:10.1016/j.jebo.2023.09.025

Religion and cooperation across the globe

2023· article· en· W4387431662 on OpenAlexafffund
Felipe Valencia Caicedo, Thomas Dohmen, Andreas Pondorfer

Bibliographic record

VenueJournal of Economic Behavior & Organization · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaDeutsche Forschungsgemeinschaft
KeywordsProsocial behaviorAltruism (biology)BuddhismReciprocity (cultural anthropology)GlobePositive economicsSocial psychologyGeneral Social SurveyChristianityPunishment (psychology)SociologyHinduismWorld Values SurveyJudaismPsychologyEconomicsReligious studies

Abstract

fetched live from OpenAlex

Social science research has stressed the important role of religion in sustaining cooperation among non-kin. We contribute to this multidisciplinary literature with a large-scale empirical study documenting the relationship between religion and cooperation. We analyze newly available, experimentally validated, and globally representative data on social preferences and world religions (Christianity, Islam, Hinduism, Buddhism and Judaism). We find that individuals who report believing in such religions exhibit more prosocial preferences, as measured by their levels of positive reciprocity, altruism and trust. We further document heterogeneous patterns of negative reciprocity and punishment—two key elements for cooperation—across world religions. The association between religion and prosocial preferences is stronger in more populous societies and weaker in countries with better formal institutions. The interactive results between these variables point again towards the substitutability between religious and secular institutions, when it comes to sustaining 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2023
Admission routes2
Has abstractno

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