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Record W2994857692 · doi:10.1038/s41562-019-0812-2

Author Correction: A consensus-based transparency checklist

2019· article· en· W2994857692 on OpenAlexaff
Balázs Aczél, Barnabás Szászi, Alexandra Sarafoglou, Zoltán Kekecs, Šimon Kucharský, Daniel J. Benjamin, Chris Chambers, Agneta Fisher, Andrew Gelman, Morton Ann Gernsbacher, John P. A. Ioannidis, Eric J. Johnson, Kai J. Jonas, Stavroula Kousta, Scott O. Lilienfeld, D. Stephen Lindsay, Candice C. Morey, Marcus R. Munafò, Ben R. Newell, Harold Pashler, David R. Shanks, Daniel J. Simons, Jelte M. Wicherts, Dolores Albarracín, Nicole D. Anderson, John Antonakis, Hal R. Arkes, Mitja D. Back, George C. Banks, Christopher G. Beevers, Andrew Bennett, Wiebke Bleidorn, Ty W. Boyer, Cristina Cacciari, Alice S. Carter, Joseph Cesario, Charles Clifton, Ronán Conroy, M. E. Cortese, Fiammetta Cosci, Nelson Cowan, Jarret T. Crawford, Eveline A. Crone, John J. Curtin, Randall W Engle, Simon Farrell, Pasco Fearon, Mark Fichman, Willem E. Frankenhuis, Alexandra M. Freund, M. Gareth Gaskell, Roger Giner‐Sorolla, Don P. Green, Robert L. Greene, Lisa L. Harlow, Fernando Hoces de la Guardia, Derek M. Isaacowitz, Janet L. Kolodner, Debra Lieberman, Gordon D. Logan, Wendy Berry Mendes, Lea Moersdorf, Brendan Nyhan, Jeffrey M. Pollack, Christopher J. Sullivan, Simine Vazire, Eric‐Jan Wagenmakers

Bibliographic record

VenueNature Human Behaviour · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsBaycrest HospitalUniversity of Victoria
FundersNational Institute of Mental Health
KeywordsChecklistTransparency (behavior)Computer scienceInformation retrievalPsychologyPolitical scienceComputer securityCognitive psychology

Abstract

fetched live from OpenAlex

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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)0.148
metaresearch head score (Gemma)0.756
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: Other · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.782

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1480.756
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0130.007
Science and technology studies0.0090.006
Scholarly communication0.0150.007
Open science0.0070.009
Research integrity0.0150.022
Insufficient payload (model declined to judge)0.0850.042

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 head0.080
GPT teacher head0.406
Teacher spread0.326 · 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
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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