Revenge Pornography and Rape Culture in Canada's Nonconsensual Distribution Case Law
Bibliographic record
Abstract
Abstract Canada criminalized the nonconsensual distribution of intimate images in 2014. Lawmakers and commentators noted that this new offense would fill a legislative gap in relation to “revenge pornography,” which entails individuals (typically men) sharing intimate images of their ex-partners (typically women) online in an attempt to seek revenge or cause them harm. Feminist writers and activists categorize revenge pornography as a symptom and consequence of “rape culture,” in which sexual violence is routinely trivialized and viewed as acceptable or entertaining, and women are blamed for their sexual victimization. In this chapter, I analyze Canada's burgeoning revenge pornography case law and find that these cases support an understanding of revenge pornography as a serious form of communal, gendered, intimate partner violence, which is extremely effective at harming victims because of broader rape culture. While Canadian judges are taking revenge pornography seriously, there is some indication from the case law that they are at risk of relying on gendered reasoning and assumptions previously observed by feminists in sexual assault jurisprudence, which may have the result of bolstering rape culture, rather than contesting it.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".