'Revenge Porn,' Tort Law, and Changing Socio-Technological Realities: A Commentary on Doe 464533 v ND
Bibliographic record
Abstract
In the 2016 decision of Doe 464533 v ND, the Ontario Superior Court further developed the common law tort of invasion of privacy in Ontario. The decision concerned what is colloquially known as “revenge porn.” The initial finding of liability on the part of the defendant was celebrated by legal experts, who praised the Court’s clarification of the contours of this tort. The Doe decision, while being of limited precedential value, serves as a fruitful site of exploration for the potentiality of Canadian tort law to effectively respond to cases dealing with potential breaches of privacy, particularly as they occur in the digital sphere. This article examines the Doe decision with the view that the invasion of privacy tort in Canada ought to develop so as to effectively respond to instances of “revenge porn” and unforeseen breaches of privacy that occur thanks to technological and social change. This comment proceeds in three parts, beginning with a brief contextual explanation of the facts and law at play. Second, it explores the shortcomings of tort of public disclosure of private facts with respect to unforeseen breaches of privacy that occur due to technological and social change. This comment concludes by considering the possibility of utilizing a “reasonable expectation of privacy” test, in order to ensure the tort’s survival and due to numerous key advantages of such an approach.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.050 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.043 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".