Bibliographic record
Abstract
Although the Supreme Court of Canada’s reasoning in Doré makes it difficult to categorize in terms of the standard positions in the proportionality debates, it is open to challenges that are directed at the precision, coherence and accuracy of the reasoning. In the first part of the paper, the author articulates these challenges. In the second part, the author shows in detail how the reasoning in Doré departed from the standard academic debates about the concept of proportionality, but argues that when the Court engaged a cognate set of debates, its reasoning was unconvincing. In the author’s view, the Court could have profitably avoided these debates and focused instead on (1) crafting a decision that avoided the pitfalls identified in the first part of the paper; and (2) evaluating the consequences of its reasons. The paper begins by setting out the facts and reasons in Doré. The concerns raised in the first two parts of this paper address questions of judicial craft, and the paper concludes by suggesting that the reasons of the Court would have been stronger if they had focused on these questions and not on academic debates.
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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".