Systemic Wrongdoing, Public Authority Liability, and the Explanatory Function of Tort Law: Two Case Studies
Bibliographic record
Abstract
The narrative character of tort law is fundamental to its content and function. The narratives of tort doctrine identify wrongful/risk-creating conduct, and explain that conduct as being within the control of a defendant in a way that justifies civil liability and compensation. The explanations provided by tort doctrine instruct us how to avoid the wrongs, risks, and harms they describe; this explanatory function is essential to both deterrence and fairness. Liability without coherent explanation is both unjust and ineffectual (in terms of deterrence) but the failure to provide a coherent explanation, where warranted on the basis of the facts and the principles they engage, is equally unjust. In this paper, we suggest that the doctrines of negligence and vicarious liability do not provide a complete account or explanation of the different ways in which public institutions create risk, act wrongfully, and cause harm, and explore the potential for the development of alternative tort doctrines that would provide complimentary explanations of justified liability. These issues are examined in relation to two “case studies”: The Report of the Coroner into the Death of Edward Snowshoe and The Report of the Correctional Investigator of Canada into the Death of Ashley Smith.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.006 |
| 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".