Bibliographic record
Abstract
In Douglas v Hello! Ltd (No 3), the Court of Appeal noted that one ramification of ‘shoehorning’ invasions of privacy into the cause of action of breach of confidence is that ‘it does not fall to be treated as a tort under English law’. In contrast, this article contends that English courts should explicitly recognise and develop a framework for a tort of privacy, and outlines one possible version—comprising both privacy interests and the elements of the potential tort. The framework draws upon longstanding Canadian and United States jurisprudence, as well as recent fascinating Australasian decisions that have grappled with privacy claims. In reality, breach of confidence is becoming an unrecognizable cousin of the creature which Megarry J described in Coco v AN Clark (Engineers) Ltd in 1969. If, however, it is to be buttressed by a judicially‐created tort of privacy, then that tort's elements must be capable of being feasibly articulated and applied.
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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.057 | 0.065 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".