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Record W4403203719 · doi:10.1038/s44271-024-00135-z

Methodological concerns underlying a lack of evidence for cultural heterogeneity in the replication of psychological effects

2024· article· en· W4403203719 on OpenAlexafffund
Robin Schimmelpfennig, Rachel Spicer, Cindel White, Will M. Gervais, Ara Norenzayan, Steven Heine, Joseph Henrich, Michael Muthukrishna

Bibliographic record

VenueCommunications Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British ColumbiaYork University
FundersTempleton World Charity FoundationCanadian Institute for Advanced ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsReplication (statistics)PsychologyAffect (linguistics)Variation (astronomy)Social psychologyExploratory researchCognitive psychologySample (material)Sample size determinationCultural diversityMeta-analysisPower (physics)SociologySocial scienceBiologyStatisticsAnthropologyMedicineCommunication

Abstract

fetched live from OpenAlex

The multi-site replication study, Many Labs 2, concluded that sample location and setting did not substantially affect the replicability of findings. Here, we examine theoretical and methodological considerations for a subset of the analyses, namely exploratory tests of heterogeneity in the replicability of studies between "WEIRD and less-WEIRD cultures". We conducted a review of literature citing the study, a re-examination of the existing cultural variability, a power stimulation for detecting cultural heterogeneity, and re-analyses of the original exploratory tests. Findings indicate little cultural variability and low power to detect cultural heterogeneity effects in the Many Labs 2 data, yet the literature review indicates the study is cited regarding the moderating role of culture. Our reanalysis of the data found that using different operationalizations of culture slightly increased effect sizes but did not substantially alter the conclusions of Many Labs 2. Future studies of cultural heterogeneity can be improved with theoretical consideration of which effects and which cultures are likely to show variation as well as a priori methodological planning for appropriate operationalizations of culture and sufficient power to detect effects.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
grokMetaresearch
Domain: Reproducibility · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
opusMetaresearch
Domain: Reproducibility · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.652
metaresearch head score (Gemma)0.814
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.348
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6520.814
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0070.005
Science and technology studies0.0060.014
Scholarly communication0.0070.007
Open science0.0060.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.959
GPT teacher head0.723
Teacher spread0.236 · 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

Labeled directly by 3 models reading the full record.

Study designOther design
DomainMethods · Reproducibility
GenreMethods · Empirical

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

Citations4
Published2024
Admission routes2
Has abstractyes

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