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Record W3087782891 · doi:10.1038/s41586-021-03655-4

Treatment of missing data determined conclusions regarding moralizing gods

2021· letter· en· W3087782891 on OpenAlexaff
Bret Beheim, Quentin D. Atkinson, Joseph Bulbulia, Will M. Gervais, Russell D. Gray, Joseph Henrich, Martin Lang, M. Willis Monroe, Michael Muthukrishna, Ara Norenzayan, Benjamin Grant Purzycki, Azim Shariff, Edward Slingerland, Rachel Spicer, Aiyana K. Willard

Bibliographic record

VenueNature · 2021
Typeletter
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of British Columbia
FundersMax-Planck-Institut für Evolutionäre Anthropologie
KeywordsChemistryComputational biologyBiology

Abstract

fetched live from OpenAlex

Supplementary data are available at https://github.com/babeheim/moralizing-gods-reanalysis . All software is freely available under Creative Commons License CC BY-NC-SA 4.0. Source materials are available at http://seshatdatabank.info .

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)0.275
metaresearch head score (Gemma)0.595
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2750.595
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0080.012
Science and technology studies0.0050.008
Scholarly communication0.0070.009
Open science0.0070.007
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0170.003

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 head0.064
GPT teacher head0.366
Teacher spread0.302 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSimulation or modeling
DomainMethods
GenreCommentary

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

Citations90
Published2021
Admission routes1
Has abstractyes

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