Punishing ‘Revenge Porn’: Legal Interpretations of and Responses to Non-Consensual Intimate Image Distribution in Canada
Bibliographic record
Abstract
The act of distributing nude or sexually explicit images without consent-often colloquially referred to as "revenge porn"-has become an issue of popular concern and the product of frequent news headlines both in Canada and internationally.Activist and academic work has largely focused on gaining recognition of the harms of nonconsensual intimate image distribution (NCIID) and campaigning for its specific criminalization; yet, little research has been done to analyze how this act is actually being responded to in the everyday practice of the law and to determine the broader social impacts of these legal responses to and understandings of NCIID.Thus, in this dissertation, I undertake a critical discourse analysis of NCIID case law in Canada.Through this research, I map and analyze the ongoing legal construction of and response to NCIID and trouble many of the prevailing assumptions regarding the nature of NCIID and the efficacy of legal responses to this act.My discursive analysis of legal responses to NCIID utilizes anti-carceral, pro-sex, intersectional, and critical legal feminisms (along with queer theory and theorizations of technology and photography) to determine what NCIID is "coming to mean" (Crocker 2008, 90) in law.I find that NCIID is a complex issue involving: a diversity of victims and offenders, a range of harms, a plethora of potential framings, and a variable relationship to digital technologies.Ultimately, I argue that an analysis of the efficacy of responding to NCIID in law must consider this diversity of cases and the socio-legal impacts of various constructions of and responses to NCIID.My research finds that Canadian judges have largely understood NCIID as an extremely harmful act requiring denunciation and deterrence (often via incarceration).Although this may be understood by many "revenge porn" activists and researchers as an unproblematic development that demonstrates the effectiveness of legal responses to NCIID, I argue that it is necessary to critically analyze the unexpected consequences and shortcomings of responding to NCIID through law.expression have dovetailed and contributed to the pervasive panic associated with both non-consensual and consensual nude image sharing amongst youth (Ringrose et al. 2012,
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.063 | 0.054 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".