Bibliographic record
Abstract
Previous articleNext article FreeNotes on ContributorsFull TextPDF Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreSukaina Hirji is an assistant professor in the Department of Philosophy at the University of Pennsylvania. Her research explores questions of virtue and value, both in Aristotle and in contemporary moral philosophy. [email protected]Christopher Howard is assistant professor of philosophy at McGill University. [email protected]Andrew Huddleston is reader in philosophy at Birkbeck, University of London. His work focuses on nineteenth- and twentieth-century European philosophy (esp. Nietzsche), aesthetics, and ethics. He is the author of Nietzsche on the Decadence and Flourishing of Culture (2019). [email protected]Theron Pummer is a senior lecturer in philosophy at the University of St Andrews. He works on problems in ethical theory, metaphysics, and practical ethics. He recently completed a book titled The Rules of Rescue: Cost, Distance, and Effective Altruism (forthcoming). [email protected]Paul Russell is professor of philosophy at the University of British Columbia and at Lund University, where he is also director of the Lund|Gothenburg Responsibility Project (LGRP). His published work includes The Limits of Free Will: Selected Essays (2017). Previous articleNext article DetailsFiguresReferencesCited by Ethics Volume 131, Number 4July 2021 Article DOIhttps://doi.org/10.1086/713946 Views: 687 © 2021 by The University of Chicago. All rights reserved. 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.798 | 0.694 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".