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Record W2320057365 · doi:10.1037/h0099277

Reproductive strategy and ethnic conflict: Slow life history as a protective factor against negative ethnocentrism in two contemporary societies.

2011· article· en· W2320057365 on OpenAlexaff
Aurelio José Figueredo, Dok J. Andrzejczak, Daniel N. Jones, Vanessa Smith‐Castro, Eiliana Montero

Bibliographic record

VenueJournal of Social Evolutionary and Cultural Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEthnocentrismEthnic conflictEthnic groupSocial psychologySociologyPsychologyAnthropology

Abstract

fetched live from OpenAlex

Much previous theory and evidence in both social and evolutionary psychology has been equivocal and inconsistent regarding whether in-group altruism should predict out-group hostility, and whether this effect should be positive or negative in direction. A “slow” Life History (LH) strategy emphasizes both kin-selected altruism and reciprocal altruism as means of investing heavily in offspring, blood relatives, and mutualistic social relationships with both kith and kin. We therefore investigated whether a slow LH strategy, as a measurable individual-difference variable favoring in-group altruism (positive ethnocentrism), should predict out-group hostility (negative ethnocentrism), and what the direction of the hypothesized effect would be. We found that a multivariate latent variable representing slow LH strategy served as a protective factor against a latent variable representing Negative Ethnocentrism. These results were replicated in the United States of America and in the Republic of Costa Rica using Multisample Structural Equation Model with cross-sample equality constraints.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.183
GPT teacher head0.397
Teacher spread0.215 · 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 teacher head, not a consensus.

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

Citations48
Published2011
Admission routes1
Has abstractyes

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