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Record W2291698658 · doi:10.1126/science.aad9163

Response to Comment on “Estimating the reproducibility of psychological science”

2016· letter· en· W2291698658 on OpenAlexaff
Christopher Anderson, Štěpán Bahník, Michael Barnett‐Cowan, Frank A. Bosco, Jesse Chandler, Christopher R. Chartier, Felix Cheung, Cody D. Christopherson, Andreas Cordes, Edward Cremata, Nicolás Della Penna, Vivien Estel, Anna Fedor, Stanka A. Fitneva, Michael C. Frank, James A. Grange, Joshua K. Hartshorne, Fred Hasselman, Felix Henninger, Marije van der Hulst, Kai J. Jonas, Calvin K. Lai, Carmel Levitan, Jeremy K. Miller, Katherine Sledge Moore, Johannes Meixner, Marcus R. Munafò, Koen Ilja Neijenhuijs, Gustav Nilsonne, Brian A. Nosek, Franziska Plessow, Jason M. Prenoveau, Ashley A. Ricker, Kathleen Schmidt, Jeffrey R. Spies, Stefan Stieger, Nina Strohminger, Gavin Brent Sullivan, Robbie C. M. van Aert, Marcel A. L. M. van Assen, Wolf Vanpaemel, Michelangelo Vianello, Martin Voracek, Kellylynn Zuni

Bibliographic record

VenueScience · 2016
Typeletter
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's UniversityUniversity of Waterloo
FundersLaura and John Arnold FoundationJohn Templeton Foundation
KeywordsReproducibilityPessimismPsychological sciencePsychologyPsychological researchOpen scienceStatisticsSocial psychologyMathematicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Gilbert et al. conclude that evidence from the Open Science Collaboration's Reproducibility Project: Psychology indicates high reproducibility, given the study methodology. Their very optimistic assessment is limited by statistical misconceptions and by causal inferences from selectively interpreted, correlational data. Using the Reproducibility Project: Psychology data, both optimistic and pessimistic conclusions about reproducibility are possible, and neither are yet warranted.

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)0.012
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.988
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.077
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.005
Open science0.0040.003
Research integrity0.0570.049
Insufficient payload (model declined to judge)0.0110.018

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 head0.801
GPT teacher head0.608
Teacher spread0.193 · 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.

Study designNot applicable
DomainReproducibility
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

Citations197
Published2016
Admission routes1
Has abstractyes

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