A Matter of Motive: Malice in the Law of Torts in the Age of Connectivity
Bibliographic record
Abstract
To meet the challenges posed by the novel modes of interpersonal relationships of contemporary society, Canadian tort law must develop a general principle of liability for the intentional infliction of harm. This principle would recognize the normatively-significant common thread of the wrongdoer’s intention to cause harm to another person in phenomena as varied as doxing, swatting, revenge porn, cyberstalking, impersonation, trolling, and harassment. The recent development of discrete, context-specific torts in response to problematic social media conduct is an inherently limited approach to novel interpersonal conduct. However, it also offers an opportunity for the enunciation of a general principle of liability for the intentional infliction of harm. Doing so would allow courts to do justice in novel factual circumstances through the coherent, principled, and consistent imposition of liability. The alternative, ad hoc responses to novel harms, will inevitably be narrow, incoherent, and unsustainable.\nPour relever les défis posés par les nouveaux modes de relations interpersonnelles de la société contemporaine, le droit canadien de la responsabilité civile doit élaborer un principe général de responsabilité pour l’infliction intentionnelle d’un préjudice. Ce principe reconnaîtrait le point commun, significatif sur le plan normatif, de l’intention de l’auteur du délit de causer un préjudice à une autre personne dans des phénomènes aussi variés que la pornographie de vengeance, l’usurpation d’identité et les différentes formes de harcèlement, dont le cyberharcèlement. Le développement récent de délits civils discrets et spécifiques au contexte en réponse aux comportements problématiques sur les médias sociaux est une approche intrinsèquement limitée aux nouveaux comportements interpersonnels. Cependant, il offre également l’occasion d’énoncer un principe général de responsabilité pour l’infliction intentionnelle d’un préjudice. Cela permettrait aux tribunaux de rendre justice dans des circonstances factuelles inédites en imposant une responsabilité cohérente, fondée sur des principes et constante. L’autre option, à savoir des réponses ad hoc aux nouveaux préjudices, sera inévitablement étroite, incohérente et insoutenable.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".