Bibliographic record
Abstract
In Saadati v Moorhead [2017] 1 SCR 543, the Supreme Court of Canada has continued the restatement of psychiatric injury law begun in Mustapha v Culligan of Canada Ltd [2008] 2 SCR 114 by rejecting the need for damage in the form of recognisable psychiatric illness as a condition of liability. This is a major departure from the law as previously understood in Canada. In contrast, the need for recognisable psychiatric illness is an essential requirement of the law in Australia and England, though not in the United States. This article traces the history of the alternative approach in Canada and notes that deviations from the traditional orthodoxy can be found not only in Canada but also in other jurisdictions. It also explores some other significant aspects of the decision.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.022 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".