Bibliographic record
Abstract
Writing a history of censorship makes one conscious of all the people who work to bring books into the world, many of whom have been on the front lines in the struggle between subversion and control.We would like to thank Rob Davies of British Library Publishing for thinking of this book, giving us the opportunity to write it, and allowing it room to grow.He has been receptive and supportive at every step.We are grateful to our editor Jon Crabb and copy-editor Jacqueline Harvey for making the text pristine (and sorry for the headaches that our endnotes will have given them), to image editors Pauline Hubner and Sally Nicholls for the illustrations, to Jason Anscomb for the visual style, as well as the typesetter, proofreader, and indexer.Thanks to Emma O'Bryen, Maria Vassilopoulos and Abbie Day for publicity, marketing and sales; and thanks to Natalie
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.497 | 0.342 |
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".