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Record W4407918365 · doi:10.2139/ssrn.5152665

The Sources of Researcher Variation in Economics

2025· preprint· en· W4407918365 on OpenAlexaff
Nick Huntington‐Klein, Claus C. Pörtner, Yubraj Acharya, Matúš Adamkovič, Joop Adema, Lameck Ondieki Agasa, Imtiaz Ahmad, Mevlude Akbulut‐Yuksel, Martin Eckhoff Andresen, David Angenendt, Andreu Arenas, Erkmen Giray Aslım, Stanislav Avdeev, Andrew Bacher-Hicks, Bradley J. Baker, Imesh Nuwan Bandara, A. Bansal, David Bartram, Katarzyna Bech-Wysocka, Andu Berha, Inés Berniell, Moiz Bhai, Markus Bjoerkheim, Jeffrey R. Bloem, Margaret Brehm, Florent Buisson, Pralhad Burli, Andrew Camp, Nicola Cerutti, Weiwei Chen, J.D. Clement, Matthew Collins, Lee Crawfurd, John Cullinan, Lachlan Deer, Reid Dorsey-Palmateer, Nicolas Duquette, Diego Mariño Fages, Grace Falken, Christine Farquharson, Jan Feld, Yevgeniy Feyman, Nathan Fiala, Anne Fitzpatrick, Andrey Fradkin, Evaewero French, Wei Fu, Luca Fumarco, S. Gallegos, Julio Gal aacute rraga, Aaron Gamino, Romain Gauriot, Victor Gay, Savas Gayaker, Jules Gazeaud, Alexandra de Gendre, Gregory Gilpin, Daniele Girardi, Dan Goldhaber, Mark N. Harris, Blake Heller, Øystein Hernæs, Andrew J. Hill, Felix Holzmeister, Martijn Huysmans, M. Saad Imtiaz, Anil Jain, Niklas Jakobsson, José Kaire, Kalyan Kumar Kameshwara, Daniel H. Karney, S. Kim, Valentin Klotzbücher, Christoph Kronenberg, Dan LaFave, David M. Lang, Maxime Liégey, D. Leann Long, Gabriele Mari, Ian McCarthy, Laura Meinzen-Dick, Erik Merkus, Klaus M. Miller, Lukas Mogge, Sanaullah Murad, Rafiuddin Najam, Elias Naumann, J. N. Nmadu, Gorkem Turgut Ozer, Jayash Paudel, Filippos Petroulakis, Christian Peukert, M.Shet Prakash, Daniel Putman, Veeshan Rayamajhee, Obeid Ur Rehman, Anna Reuter, Fernando Rios‐Avila, Julian Roeckert, Aparna Samudra, Vassiki Sanogo, Orkhan Sariyev, Joel E. Segel, Mike Smet, Krzysztof Szczygielski, Hüseyin Taştan, Martin Trombetta, Madhavi Venkatesan, Eden Volkov, Gary A. Wagner, Yue Wang, Tom Waters, Edward C. Weber, Kristina S. Weißmüller, Kristina S. Weißmüller, Kevin Williams, X Ye, Muhammad Umer Zahid, Raffaele Zanoli

Bibliographic record

VenueSSRN Electronic Journal · 2025
Typepreprint
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsToronto Metropolitan UniversityUniversity of AlbertaDalhousie University
Fundersnot available
KeywordsVariation (astronomy)EconomicsPhysics

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)0.032
metaresearch head score (Gemma)0.181
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.181
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.011
Science and technology studies0.0010.004
Scholarly communication0.0080.006
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.331
GPT teacher head0.529
Teacher spread0.198 · 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 designObservational
DomainMethods
GenreEmpirical

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
Published2025
Admission routes1
Has abstractno

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