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Record W2784547979 · doi:10.1038/s41562-018-0311-x

Justify your alpha

2018· article· en· W2784547979 on OpenAlexafffund
Daniël Lakens, Federico Adolfi, Casper J. Albers, Farid Anvari, Matthew A J Apps, Shlomo Argamon, Thom Baguley, Raymond Becker, Stephen D. Benning, Daniel E. Bradford, Erin Michelle Buchanan, Aaron R. Caldwell, Ben Van Calster, Rickard Carlsson, Sau-Chin Chen, Bryan Chung, Lincoln Colling, Gary S. Collins, Zander Crook, Emily S. Cross, Sameera Daniels, Henrik Danielsson, Lisa M. DeBruine, Daniel J. Dunleavy, Brian D. Earp, Michele I. Feist, Jason D. Ferrell, James G. Field, Nicholas W. Fox, Amanda Friesen, Caio Gomes, Mónica González-Márquez, James A. Grange, Andrew P. Grieve, Robert Guggenberger, James T. Grist, Anne‐Laura van Harmelen, Fred Hasselman, Kevin D. Hochard, Mark R. Hoffarth, Nicholas P. Holmes, Michael Ingre, Peder Mortvedt Isager, Hanna Isotalus, Christer Johansson, Konrad Juszczyk, David A. Kenny, Ahmed A. Khalil, Barbara Konat, Junpeng Lao, Erik Gahner Larsen, Gerine M. A. Lodder, Jiří Lukavský, Christopher R. Madan, David Manheim, Stephen R. Martin, Andrea E. Martin, Deborah G. Mayo, Randy J. McCarthy, Kevin McConway, Colin McFarland, Amanda Q. X. Nio, Gustav Nilsonne, Cilene Lino de Oliveira, Jean‐Jacques Orban de Xivry, Sam Parsons, Gerit Pfuhl, Kimberly A. Quinn, John J. Sakon, S. Adil Sarıbay, Iris K. Schneider, Manojkumar Selvaraju, Zsuzsika Sjoerds, Samuel G. Smith, Tim Smits, Jeffrey R. Spies, Vishnu Sreekumar, Crystal N. Steltenpohl, Neil Stenhouse, Wojciech Świątkowski, Miguel A. Vadillo, Marcel A. L. M. van Assen, Matt N Williams, Samantha E. Williams, Donald R. Williams, Tal Yarkoni, Ignazio Ziano, Rolf A. Zwaan

Bibliographic record

VenueNature Human Behaviour · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsUniversity of British Columbia
FundersUCB CelltechNational Institutes of HealthEconomic and Social Research CouncilRWTH Aachen UniversityUniversität BielefeldLeids Universitair Medisch CentrumOnderzoeksraad, KU LeuvenKU LeuvenLinköpings UniversitetDirectorate for Biological SciencesConselho Nacional de Desenvolvimento Científico e TecnológicoBangor UniversityNIHR Oxford Biomedical Research CentreConsejo Nacional de Investigaciones Científicas y TécnicasNederlandse Organisatie voor Wetenschappelijk OnderzoekTrent UniversityFlinders UniversityTechnische Universiteit EindhovenUniversiteit LeidenYale UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of GlasgowBiotechnology and Biological Sciences Research CouncilPurdue UniversityCancer Research UKRijksuniversiteit GroningenLinnéuniversitetetKeele UniversityNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchComunidad de MadridNottingham Trent UniversityTulane UniversityUniversity of Wisconsin-MadisonWest Virginia UniversityFlorida State UniversityAlexander von Humboldt-StiftungUniversity of OxfordUniversity of EdinburghNational Institute for Health and Care ResearchUniversity of Louisiana at LafayetteNational Science Foundation
KeywordsAlpha (finance)PsychologyComputer scienceClinical psychology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.018
metaresearch head score (Gemma)0.320
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.719

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.320
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.004
Science and technology studies0.0050.005
Scholarly communication0.0160.006
Open science0.0030.004
Research integrity0.0210.022
Insufficient payload (model declined to judge)0.2150.283

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 head0.194
GPT teacher head0.502
Teacher spread0.307 · 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 designTheoretical or conceptual
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

Citations492
Published2018
Admission routes2
Has abstractno

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