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Record W2325122960 · doi:10.5751/es-07424-200208

Breeding cooperation: cultural evolution in an intergenerational public goods experiment

2015· article· en· W2325122960 on OpenAlexvenueno aff
Vicken Hillis, Mark Lubell

Bibliographic record

VenueEcology and Society · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublic goodBusinessNatural resource economicsEnvironmental resource managementFisheryEconomicsBiologyMicroeconomics

Abstract

fetched live from OpenAlex

The transmission of cooperative norms among individuals across generations plays a key role in our ability to successfully manage social-ecological systems in changing environments.Here, we use an intergenerational public goods experiment combining both cooperative advice and in-game communication in order to examine the transmission of cooperative norms across generations of experimental participants.We show that cooperative intergenerational advice has a positive impact on both (i) contributions by individuals in a subsequent generation and (ii) the cooperative content of communication among individuals in a subsequent generation.The impact of cooperative intergenerational advice is most pronounced at the beginning of the subsequent generation.The impact of in-game communication, on the other hand, is relatively consistent over the course of the experiment.Sessions combining advice and communication have the highest levels of cooperation overall.Our findings highlight the potential contributions of intergenerational experiments to research in social-ecological systems more generally.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.370
Teacher spread0.274 · 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 designBench or experimental
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

Citations8
Published2015
Admission routes1
Has abstractyes

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