Bibliographic record
Abstract
Previous article FreeContributorsPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreJohn RennerIndependent Scholar, London, United KingdomQuincy NganAssistant Professor in Art History, Yale University, New Haven, CTMary Joan Winn LeithAssociate Professor and Chair of the Department of Religious Studies, Stonehill College, Easton, MAAllyson Everingham ShecklerAssistant Professor of Art History, Stonehill College, Easton, MAVictoria AddonaPostdoctoral Fellow, McGill University, Montreal, Quebec, CanadaMichael GaudioProfessor of Art History, University of Minnesota, Minneapolis, MNFátima Bethencourt PérezAssociate Professor of Art History, University of Valladolid, SpainElizabeth SimpsonProfessor Emerita, Bard Graduate Center, New York, NY Previous article DetailsFiguresReferencesCited by Source Volume 42, Number 1Fall 2022 Sponsored by the Bard Graduate Center, New York Article DOIhttps://doi.org/10.1086/724577 © 2022 Bard Graduate Center. All rights reserved.PDF download Crossref reports no articles citing this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.826 | 0.696 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".