Bibliographic record
Abstract
Extract The completion of this book was made possible by the award of a Leverhulme Research Fellowship for one term in the spring of 2006. I am most grateful to the Leverhulme Trustees for their invaluable support. The chapters that follow incorporate substantial extracts from four recent essays. I am grateful to the editors of the University of Toronto Law Journal, the Canadian Journal of Law and Jurisprudence, the Cambridge Law Journal, and Current Legal Problems, for permission to reuse material in this way. A number of people have been kind enough to comment on parts of the book, or on the essays that preceded it. My partner June Chappell cast a lawyerly eye over most of the typescript, spotting numerous anomalies and infelicities that I would certainly have missed. I am also indebted to Trevor Allan, Mátyás Bódig, Raymond Plant, Jerry Postema, Amanda Perreau-Saussine and Veronica Rodriguez-Blanco. A more intelligent author could perhaps have accommodated their various criticisms and insights, to the considerable improvement of the book’s argument. This author, however, has had to rest content with the imperfect pages that now lie before the reader.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.474 | 0.270 |
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".