A Comparative Look at Vicarious Liability for Intentional Wrongs and Abuses of Power in Canadian Law
Bibliographic record
Abstract
This paper examines the theoretical underpinnings of vicarious liability in the Canadian common law as well as in Quebec’s civil law tradition. It then goes on to trace how each legal tradition has responded to employees’ intentional wrongdoing. More specifically, the paper analyses the scope of employment in cases of fraud, theft and sexual violence. The paper then identifies a developing trend in Quebec’s law through which judges adopt the approach of the common law in an attempt to find liability where the traditional civilian test is of no use. Ultimately, a comparison between the civilian approach and the common law approach is made. This paper argues that the common law approach is more responsive to the policies justifying the institution of vicarious liability and that Quebec courts should be more willing to adopt this.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".