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Record W4389527651 · doi:10.1002/jae.2918

Issue Information

2023· paratext· en· W4389527651 on OpenAlexaff
Barbara Rossi, Marco Del Negro, Éric Ghysels, Michael W. McCracken, Herman K. van Dijk, Francis Vella, Edward Vytlacil, John Geweke, Jerry A. Hausman, James J. Heckman, David Hendry, Guy Laroque, James G. MacKinnon, Charles F. Manski, Daniel McFadden, Alain Monfort, Adrian Pagan, Marcelle Chauvet, Gergely Ganics, Replication Section Editor, Heather Anderson, M. Hashem Pesaran, Knut Are Aastveit, Jason Abrevaya, Peter Arcidiacono, Kris Boudt, Dario Caldara, Efrem Castelnuovo, J. S. d. Chan, Yoosoon Chang, Tim Conley, Valentina Corradi, Drew Creal, Monica Dias, Todd E. Elder, Graham Elliott, Max Farrell, Sérgio Firpo, Ana Beatriz Galvão, Nikolaus Hautsch, E. Herbst, Ana María Herrera, Susumu Imai, Kris Jacobs, Koen Jochmans, Òscar Jordà, Sylvia Kauffman, Shakeeb Khan, Frank Kleibergen, Tong Li, Laura Liu, Gael M. Martin, Sophocles Mavroeidis, Leonardo Melosi, James G. Mitchell, James Morley, James M. Nason, Alexei Onatski, Harry Paarsch, Francesco Ravazzolo, Philipp Schmidt-Dengler, Tatevik Sekhposyan, Thanasis Stengos, Allan Timmermann, Bas Van Der Klaauw, Robert Vigfusson, Frank Windmeijer, Jonathan L. Wright, Cynthia Wu, Wen‐Hung Liao, Jun Ma, Eiji Gotō, J Jacobs, Thomas Sinclair, Sy Pham Van, Ruhollah Eskandari, Morteza Zamanian, Daniel Lewis, Davide Melcangi, Laura Pilossoph

Bibliographic record

VenueJournal of Applied Econometrics · 2023
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsUniversity of GuelphWestern UniversityQueen's University
Fundersnot available
KeywordsComputer scienceBusiness

Abstract

fetched live from OpenAlex

No abstract is available for this article.

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9500.891

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 head0.048
GPT teacher head0.218
Teacher spread0.171 · 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
Domainnot available
GenreEditorial

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

Citations0
Published2023
Admission routes1
Has abstractyes

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