Class Action Intrusions: A Development In Privacy Rights or an Indeterminate Liability?
Bibliographic record
Abstract
Since its inception in Jones v Tsige, legal practitioners have struggled with the tort of intrusion upon seclusion. The limited damages awarded in that case, and what the court indicated would be reasonable for privacy breaches, suggested that the tort would have limited utility as a stand-alone cause of action, and may only arise in in conjunction with other claims. However, the tort has recently been used successfully, at least at the summary judgment level, particularly in the class actions context where the aggregate claims make it more feasible to rely on the tort exclusively. In the wake of The Ontario Court of Appeal’s decision in Hopkins v Kay, this paper examines intrusion upon seclusion in the context of privacy breaches in the healthcare sector. This work purports to show that although a statutory regime exists to govern healthcare privacy breaches in Ontario and other provinces in Canada, intrusion upon seclusion is the best method for addressing privacy breaches in this context.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.084 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| 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".