Bibliographic record
Abstract
he longer I am an academic, the more I realize how easily we accrue debts in the production of a single book.As I think of the five years during which I worked on this study, I am amazed at the willingness of people to share their valuable time to help me along.On my own, I could never have produced the book you are about to read.I should begin with those who lived this history.I interviewed many people, and I would like to thank them first and foremost for taking the time to share their life stories.Their names are listed in the Bibliography.But I want to extend a special thanks to Linda Sproule-Jones, who spent hours helping me get started, and Bill Black, who provided documentary evidence on human rights investigations in the early 1970s.My trip to the northern town of Smithers was for no other reason than to spend two days with Kathleen Ruff.She stands alongside Dan Hill as a key pioneer of Canada's human rights state.Many others contributed to this project.Ramona Rose at the University of British Columbia Rare Books and Special Collections went the extra mile to uncover collections that were essential to my research.My two-year odyssey to access restricted files from the attorney general's office was made easier by its liaison, Linda Canham, whose professionalism gave me some faith in the freedom of information regime.Several research assistants at the University of Alberta also contributed to this study: Greg Eklics, Tyler Moroniuk, Will Silver, and Dan Trottier.When I had to travel and needed research assistance,
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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.261 | 0.204 |
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".