Debunking the Myth of “Not My Bad”: Sexual Images, Consent, and Online Host Responsibilities in Canada
Bibliographic record
Abstract
Non-consensual distribution of intimate images has been a crime in Canada since 2015. This article argues that it is time to consider how online platforms, hosts, and fora that allow users to post sexual images either directly engage in criminal acts or incur responsibility to help suppress this illegal activity. Methods for holding businesses responsible for participating in promoting or facilitating this type of wrongdoing by users should vary according to the level of involvement and risk that attaches to a particular online business model. One method applies to businesses that specifically traffic in illegal materials; for these specific “revenge porn” businesses, we should impose direct liability, as we do in other contexts. Another method applies where the nature of the business places it at high risk for facilitating customer illegal activity and where the business profits from that wrongdoing and so faces disincentives to discourage it. The online amateur porn industry more generally falls into this category. In these cases, obligations to assist in rooting out the illegal behaviour of customers via a consent verification system is appropriate. A third method applies to all businesses that host user-generated content where unfettered user activity is less expensive than addressing complaints about content and, thus, constitutes a structural disincentive to respond. Here, mandated response to complaints about non-consensual pornography is appropriate. The article argues that while freedom of sexual expression, policies protecting intermediary immunity, and online anonymity are important and complicate solutions to this lucrative traffic in sexual images, finding principled solutions is not impossible.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".