Bibliographic record
Abstract
This book is the culmination of ten years of research, teaching, and thinking, and there are many people who have played an important role in its development.My greatest debt of gratitude is owed to my hundreds of research subjects -lawyers, clients, judges, and others -who were generous and patient as they were subjected to my interrogations.Many of them told me that they hoped that describing their knowledge and experience would be valuable for others, and I hope they see that this goal was somewhat achieved in this book.Over this period, I have received generous research funding for my various projects from the Social Science and Humanities Council of Canada, the Law Commission of Canada, and the Department of Justice, Canada.This Wnancial support has been critical, and, as a researcher, I also appreciate the moral encouragement that this type of recognition brings.A supportive yet always challenging professional community developed around me as I worked on these projects.I want to thank especially my dear friends and colleagues John Manwaring and Chris Honeyman, who along with Bernard Mayer and Gemma Smyth read and commented on earlier drafts of this book, and the "data chicks" for many instructive and inspiring conversations.I received terriWc support and assistance from a galaxy of student researchers during both the gestation and the writing of this book.For helping me to cross the Wnal Wnish line, I extend my grateful thanks to Hena Singh and Raong Phalavong.We lost our dear friend and colleague Rose Voyvodic just as I was completing this book.Rose taught me better than any other person or any learned text that in facing new challenges and complexities, the touchstone for the new lawyer must be client-responsive service: practical, humane, and dignifying.Rose will forever epitomize the very best of the new lawyer for me.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.268 | 0.203 |
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".