Bibliographic record
Abstract
This book represents an unusual co-authoring partnership, between a senior (and now emeritus) faculty member on the one hand and a graduate student on the other.Indeed, it is sufficiently unusual to call for an explanation.Marc Zanoni came to the University of Lethbridge to work on a Master of Arts degree in political science, to be supervised by Peter McCormick.After some discussion of alternatives, it was decided that the thesis topic would involve an examination of the Supreme Court of Canada's By the Court decisions -those decisions that are not attributed to any specific individual but mysteriously and cryptically to the Court.The thesis project was originally conceived in terms of the chronology, inventory, and typology found in Chapters 8 and 9 of this book.This plan quickly went off the tracks, however.A routine element of any thesis is the literature review, which situates the project in relation to the work that has already been done on the subject.The first sign that there would be nothing routine about this master's thesis was the discovery (which still surprises us every time we mention it) that there was simply no academic literature on the subject.Although most courtwatching academics are well aware that such decisions appear from time to time, and that at least some of them have been extremely important,
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.416 | 0.230 |
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".