Bibliographic record
Abstract
This preface can be short and sweet. This project began, like most of my academic undertakings, more by chance than design. After writing a couple of papers, I realized that a certain pattern was taking shape and a unifying theme was emerging. In a manner of speaking, a book was beginning to evolve. This is the final product of that trial-and-error process; there have been the usual mutations, couplings, and mistakes along the way. Some of the work saw the light of day in earlier essays in Legal Studies, Chicago-Kent Law Review, Current Legal Problems , and Irish Law Teachers . Nevertheless, the contents of the book are almost entirely original in source and style, if nothing else. As usual, many people have played important parts in helping me to complete this book. A variety of students have put in time as research assistants and have tried to keep me on the straight and narrow – Simon Lee, Nigel Marshman, Rishi Bandhu, Archana Mathew, Jim Smith, Abbas Sabur, Merel Veldius, Daved Muttart, and Luke Woodford. I have also benefited from a host of critics and colleagues, mostly friendly, who have shared their time and insights – Harry Arthurs, Derek Morgan, Richard Lucy, Tsachi Keren-Paz, Neil Duxbury, Celia Wells, Joanne Conaghan, Michael Freeman, Toni Williams, Francis Jay Mootz III, and John McCamus.
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 | 0.001 | 0.006 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.436 | 0.263 |
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".