A Common Law Tort of Privacy? The Challenges of Developing a Human Rights Tort
Bibliographic record
Abstract
This article will examine the evolution of a new tort – that of misuse of private information – in the courts of England and Wales. Stimulated by the introduction of the UK Human Rights Act 1998 (c 42), the English courts are moving towards recognition of a distinct tort which is capable of responding to advances in technology which give rise to increased possibilities for intrusion into the personal lives of private individuals. While such a development may seem preferable to the previous practice of “shoehorning” claims into the existing action for breach of confidence, this article will consider, with reference to recent case law in New Zealand and the Canadian province of Ontario, the challenges which recognition of torts protecting privacy rights present to traditional common law reasoning. In particular, it will examine the extent to which the constitutional framework in each jurisdiction, which provides for protection of a right to privacy and freedom of expression, has led to different responses. Developing a privacy tort is no easy task, both in defining the interest protected and determining its scope and appropriate remedial framework. In analysing how the courts have addressed these issues, the article will consider whether incremental case law development or legislative intervention is more likely to lead to the coherent evolution of this area of law.
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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.017 |
| 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".