Judicial approaches to combating ‘revenge porn’: a multi-jurisdictional perspective
Bibliographic record
Abstract
This article aims to provide a nuanced articulation of the challenges and complexities associated with the various judicial approaches countenanced across multiple jurisdictions to date toward combating the phenomenon colloquially referred to as ‘revenge porn’. The article’s central argument is that although the scope of several civil causes of action, such as breach of confidence, defamation, copyright and invasion of privacy, have been expanded in recent years to accommodate the evolving dynamics of revenge porn, a number of theoretical and practical issues nonetheless arise, which courts across various jurisdictions, including Australia, Canada, the UK, the USA, New Zealand and the Commonwealth Caribbean, have struggled to treat with. The article concludes by asserting that notwithstanding the important role played by civil causes of action in vindicating the rights of victims of revenge porn, legislative intervention remains invaluable.
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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 | 0.052 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.023 | 0.067 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".