Bibliographic record
Abstract
In many ways, this book was at least ffeen years in the making.It started with curiosity and disgust about a referendum concerning terms for treaty negotiations, quickly became a series of conference papers and articles, and eventually turned into a much larger personal and professional agenda.Along the way, we have had countless discussions with many people who have shaped our thinking, challenged our assumptions, directed us to new readings, and simply inspired us.We are grateful to all.Parts of the research that went into the book were supported by a grant from the Social Sciences and Humanities Research Council of Canada.Writing and editing were supported through a Professional Leave Award from Western Washington University.In the midst of this writing project, we were invited to contribute to the Osgoode Hall Constitutional Cases Conference in April 2019 and to publish a paper in its special issue of the Supreme Court Law Review.Tis was a particularly rewarding experience that enhanced our thinking and extended our bibliography.For this opportunity, we ofer thanks to the organizers, especially Sonia Lawrence, and to our fellow conference panel members, Craig Scott, Richard Ogden, and Scott Franks.Tanks are also due to the reviewers at Supreme Court Law Review, who were generous and helpful.Tanks also to Alexandra Flynn, Kent McNeil, and Dayna Scott.We also gratefully accepted invitations to present some of this work at the University of Lethbridge and the University of Calgary in the fall of 2019.Tese were highly enjoyable visits that featured many productive
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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 | 0.004 | 0.017 |
| 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) | 0.220 | 0.163 |
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".