Bibliographic record
Abstract
The authors were asked by the Uniform Law Conference of Canada to help create a model tort for the non-consensual distribution of intimate images (NCDII), sometimes known as revenge porn. Five Canadian provinces already have such torts, and they are relatively traditional actions for damages requiring the plaintiff to prove that the image is an identifiable image of them and fault (usually that the image was distributed by the defendant with knowledge – or recklessness – as to the absence of consent). The problem with such actions is that they are often not effective at getting victims of NCDII what they most want: quick, cheap and effective removal of an image, or an order prohibiting further distribution of an image. Injunctive relief is available, but the costs and timelines of litigation may make such orders expensive and relatively ineffective, as reputational harm will have been done before an order can be obtained. Our paper focuses on how to create a tort so as to maximize the possibility of quick, cheap and effective removal of NCDII from the internet. For example, we consider whether small claims court could achieve this through declaratory relief (intermediaries will often remove images when a court declares their publication unlawful, such that declaratory relief will sometimes be sufficient). Alternately, the rules of small claims courts could be changed to permit injunctive relief. We consider the merits of a simplified superior court action that provides only for injunctive and declaratory relief. Yet another possibility is a law, presumably federal, that simply requires intermediaries to remove or de-index intimate images on request of the person depicted in the image. We discuss the benefits and drawbacks of various proposals.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".