Privacy and the Canadian Media: Developing the New Tort of "Intrusion Upon Seclusion" with Charter Values
Bibliographic record
Abstract
With the recent recognition of the new tort of "intrusion upon seclusion", Canadian privacy law has experienced a fundamental and modernizing shift. In Jones v Tsige, the Ontario Court of Appeal held that a person is liable for an invasion of privacy, if "he or she intrudes, physically or otherwise, upon the seclusion of another or his private affairs or concerns [...] if the invasion would be highly offensive to a reasonable person." This new tort has the potential to dramatically impact society, media, and our core conceptions of individual privacy. In this paper, I engage the perspective of the Canadian media to analyze this legal shift against the competing Charter values of freedom of expression, free press, and individual privacy. I argue that in order to achieve a proper balance in this context, Canadian courts should be guided by the recent defamation law analysis from the Supreme Court of Canada in Grant v Torstar Corp. To this end, I propose a two-stage framework for principled application of the tort and suggest that in the media context, Canadian courts should recognize a principled defence of "Responsible Newsgathering on Matters of Public Interest.” This analysis only begins the debate. The introduction of this tort should encourage immediate discussion of how to best foster its growth with Charter values.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".