Privacy, Corrective Justice, and Incrementalism: Legal Imagination and the Recognition of a Privacy Tort in Ontario
Bibliographic record
Abstract
This article considers the nature of common law development as exemplified by the recent privacy case of Jones v. Tsige. The author focuses on Jones, in which the Ontario Court of Appeal recognized the novel privacy tort of “intrusion upon seclusion”. Using a detailed analysis of the case as its basis, the article explores issues which have much wider significance for the judicial development of privacy laws: the process of incremental elaboration of the law, the moral impulses at work within it, and the relevance of imagination to its operations. By drawing out these discrete issues and analyzing the role that each plays in Jones, the article offers a framework for examining such questions in future privacy cases. Moreover, this article argues that the judgment in Jones brings valuable clarity to the analysis of the process of common law development. In particular, the essay concludes that the novel privacy tort recognized in Jones is the result of a legitimate incremental development rather than an instance of undue judicial activism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.066 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".