10 Stormy “Whethers”: Ethical Challenges of a Clinician in Academia
Bibliographic record
Abstract
In order to consider the Supreme Court of Canada’s roles and functions, this article explores the interaction between judicial discretion and institutional strictures such as the Court’s size and appellate authority, chosen from a number of models. The author states and analyses the rationale for investing the Supreme Court with the power to preside over appeals arising out of all provincial appellate courts and regarding provincial matters, and argues it has become a supervisory tribunal rather than appellate court in the traditional sense. He discusses the formula for granting leave to appeal and the purpose of the Court for granting such leave. Finally, he considers how this function of the Court is affected by the kind of business that comes before it, the status of bare concurrences and the effect of the rule of law.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.066 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".