Bibliographic record
Abstract
This book, like most scholarly books, I am sure, has had a complex but enriching history, and it is ultimately, and despite the ups and downs of life, the result of many wonderful conversations and collaborations throughout the last seven years with amazing colleagues, incredibly generous and energizing students, and two exceptional editors; many visits to a series of outstanding libraries and research centres across the world; and, last but not least, the support, patience, and love of my family.I am deeply grateful to everyone who has had an impact, directly or indirectly, on the completion and publication of this book, and I wouldn't have been able to complete this project without everyone acknowledged here.At Washington University in St. Louis, I am extremely fortunate to be able to work across two fabulous departments, and, since 2018, as part of the leadership team of our Center for the Humanities.I would like to thank all my colleagues across the various sections of the Department of Romance Languages and Literatures, and particularly my various chairs (Elzbieta Sklodowska, Harriet Stone, Michael Sherberg, Andrew Brown, and Julie Singer), who have been incredibly generous and supportive of my work over the years since my arrival
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.213 |
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".