More than 'Revenge Porn' Civil Remedies for the Nonconsensual Distribution of Intimate Images
Bibliographic record
Abstract
The non-consensual distribution of intimate images, or “revenge porn” as it is colloquially known, is a growing phenomenon in the digital era that has devastated the lives of countless individuals. Targets of this conduct have suffered both short and long-lasting harms that have had serious repercussions on their mental health, physical well-being, and safety. Once their intimate images have been shared without their consent, they can face damage to their personal and professional reputations. There are reported cases where individuals have lost their jobs, have had to relocate, were stalked and harassed, experienced some form of emotional trauma, and had their privacy violated after their images were shared. It is a jarring experience to say the least. Most want the images taken off down as soon as possible, but attempting to remove the images from the Internet can be a costly endeavor. This collection of harms and damages beg for a civil remedy. As a result, some individuals have begun turning to the civil courts to seek remedies for the damages they have incurred. This paper explores some civil remedies that may be available for individuals who have had their intimate images shared without their consent, but is no way an exhaustive list of civil actions or remedies that may be available.In four parts, this paper (1) introduces and describes common manifestations of this problematic behavior, (2) outlines a selection of civil remedies that are available to individuals who have had their intimate images shared without their consent, (3) reviews some remedies and relief that may be available; and (4) provides useful practice tips for lawyers serving clients who have had their intimate images shared without consent. It also includes an appendix with information on civil legislation in other Canadian jurisdictions, the criminal response to this behavior, and links to organizations that are addressing the non-consensual distribution of intimate images.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".