Thin-Skull Plaintiffs, Socio-Cultural "Abnormalities" and the Dangers of an Objective Test for Hypersensitivity
Bibliographic record
Abstract
The extent to which "hypersensitivity" can serve as a legal basis for demanding additional compensation has always been a controversial issue in tort law. A key challenge facing courts lies in determining how the "thin-skull rule," traditionally related to physical conditions that predispose an individual to additional injury, can be applied to claims from "hypersensitive" plaintiffs citing personality-linked vulnerabilities of a religious, socio-cultural, or psychiatric nature. This article critically evaluates the viability of the "ordinary-fortitude test" adopted by the Supreme Court of Canada in Mustapha v. Culligan, and discusses the relative merits of a "multi-factorial test" in determining the admissibility of personalitylinked "thin-skull claims." In this regard, a fact-specific, contextual approach that considers the causalnexus between the defendant's negligence and the plaintiff's injury would provide a more flexible framework with which to measure liabilitythan an artificially-defined "one-size-fits-all" standard of "psychologicalresilience" in an increasingly multicultural Canada.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".