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Record W4214662435 · doi:10.31222/osf.io/5ecnh

Consensus-based guidance for conducting and reporting multi-analyst studies

2021· preprint· en· W4214662435 on OpenAlexafffund
Balázs Aczél, Barnabás Szászi, Gustav Nilsonne, Olmo R. van den Akker, Casper J. Albers, Marcel A. L. M. van Assen, Jojanneke A. Bastiaansen, Daniel J. Benjamin, Udo Boehm, Rotem Botvinik‐Nezer, Laura F. Bringmann, Niko A. Busch, Emmanuel Caruyer, Andrea Michael Cataldo, Nelson Cowan, Andrew Delios, Noah N'Djaye Nikolai van Dongen, Chris Donkin, Johnny van Doorn, Anna Dreber, Gilles Dutilh, Gary F. Egan, Morton Ann Gernsbacher, Rink Hoekstra, Sabine Hoffmann, Felix Holzmeister, Magnus Johannesson, Kai J. Jonas, Alexander T. Kindel, Michael Kirchler, Yoram Kevin Kunkels, D. Stephen Lindsay, Jan-Francois Mangin, Dóra Matzke, Marcus R. Munafò, Ben R. Newell, Brian A. Nosek, Russell A. Poldrack, Don van Ravenzwaaij, Jörg Rieskamp, Matthew Salganik, Alexandra Sarafoglou, Tom Schönberg, Martin Schweinsberg, David R. Shanks, Raphael Silberzahn, Daniel J. Simons, Bobbie Spellman, Jeffrey J. Starns, Samuel St‐Jean, Eric Luis Uhlmann, Jelte M. Wicherts, Eric‐Jan Wagenmakers

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of AlbertaUniversity of Victoria
FundersFonds de recherche du Québec – Nature et technologiesTempleton Religion TrustNederlandse Organisatie voor Wetenschappelijk OnderzoekWeizmann Institute of ScienceNatural Sciences and Engineering Research Council of CanadaEuropean CommissionArnold VenturesTempleton World Charity FoundationJohn Templeton Foundation
KeywordsRobustness (evolution)Data scienceComputer scienceManagement sciencePsychologyEngineering

Abstract

fetched live from OpenAlex

We present consensus-based guidance for conducting and documenting multi-analyst studies. We discuss why broader adoption of the multi-analyst approach will strengthen the robustness of results and conclusions in empirical sciences.

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)0.533
metaresearch head score (Gemma)0.855
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.467
Threshold uncertainty score0.576

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5330.855
Meta-epidemiology (narrow)0.0070.010
Meta-epidemiology (broad)0.0110.014
Bibliometrics0.0300.022
Science and technology studies0.0070.008
Scholarly communication0.0220.015
Open science0.0170.014
Research integrity0.0320.028
Insufficient payload (model declined to judge)0.0390.047

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 head0.972
GPT teacher head0.667
Teacher spread0.305 · 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 designTheoretical or conceptual
DomainReporting
GenreMethods

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

Citations9
Published2021
Admission routes2
Has abstractyes

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