Trading Nudes Like Hockey Cards: Exploring the Diversity of ‘Revenge Porn’ Cases Responded to in Law
Bibliographic record
Abstract
Popular and scholarly responses to nonconsensual pornography (colloquially known as ‘revenge porn’) have largely, though not exclusively, focused on cases that fit within the paradigmatic mold of men nonconsensually distributing intimate images with the intention to harass or abuse their female partners/ex-partners. However, several recent studies offer evidence that the dynamics of this act are more diverse than previously assumed. In this article I analyze 49 Canadian legal cases to determine the extent to which those cases that make it to the court level fit within the typical framing and to explore the dynamics of cases laying outside this paradigm. I find that, while a large portion of cases fit the commonly imagined pattern, the case law also includes several cases that complicate dominant framings of nonconsensual pornography. Using intersectional and postmodern feminist theory, I argue that this variety of case contexts necessitates more diverse socio-legal understandings of and responses to nonconsensual pornography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".