Bibliographic record
Abstract
This book is a revised, expanded, and updated version of my DPhil thesis (2007–11). I would like to thank all those who made it possible to bring the monograph to fruition, especially my graduate supervisors, Ed Bispham, Anna Clark, and Simon Price. The comments of my examiners and assessors, Neil McLynn, John North, Jonathan Prag, and Nicholas Purcell, were tremendously helpful. John North, Nicholas Purcell, and an anonymous peer-reviewer for OUP painstakingly read the manuscript in draft and improved it immeasurably. Any errors that remain are of course my own. Many colleagues offered help, advice, and encouragement along the way, including Boris Chrubasik, Esther Eidinow, Michael Flower, Andrew Gregory, Andrew Lintott, Wolfgang de Melo, Teresa Morgan, Lucia Nixon, Scott Noegel, Christopher Pelling, Catherine Steel, Peter Toohey, the scholars of the Festus Lexicon Project and of the Fragments of the Roman Republican Orators, and many other patient colleagues and friends in Oxford, Nottingham, and Calgary. Papers relating to this project were presented at seminars or conferences in Ann Arbor, Calgary, Cambridge, Kavala, Leeds, London, Oxford, Princeton, and Quebec City, and I am grateful to the organizers and participants at these events for their stimulating feedback.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.449 | 0.301 |
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".