Bibliographic record
Abstract
The contributors to this volume gathered at Massey College in Toronto in April 2022 for two days of intensive presentations and discussions about the nature, significance, operation, and impact on Canada's law and politics, of section 33 of the Charter, the notwithstanding clause.All understood that the object of our inquiry was important and of consequence for the country and all were committed to an open-minded, if also occasionally passionate, engagement with one another on a subject that has divided scholars, politicians, and citizens alike.The result of that engagement is contained in the pages of this volume.It is to the contributors, first and foremost, that I give thanks, to their willingness to consider each other's arguments, critiques, and insights, and to their generosity in making well-considered revisions to their respective chapters in light of those arguments, critiques, and insights.In this connection, it must also be acknowledged that the peer review process resulted in considerable improvements to this volume and, while the identities of the readers in that process remain unknown to me, I would be remiss not to thank and acknowledge them nonetheless for their meticulous reading, commentary, and critiques.I am thankful to Nathalie Des Rosiers who, as principal of Massey College, both graciously welcomed us and allowed us to convene at Massey, and also contributed substantively in our proceedings and discussions.I am especially
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.286 | 0.177 |
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".