The Gendered Dimensions of Sexting: Assessing the Applicability of Canada's Child Pornography Provision
Bibliographic record
Abstract
Serious negative short- and long-term consequences can flow from teen and adolescent sexting, particularly where images are distributed beyond their intended initial recipient, and affect both the individual depicted and potentially teens and children collectively. Although some US states have prosecuted teens for child pornography offenses for both one-to-one sexting and for unauthorized redistribution of sexts, there is a dearth of reported sexting prosecutions in Canada. While there are many good reasons for Canadian legal authorities not to prosecute similarly aged teens engaged in consensual one-to-one sexting, Canada’s child pornography provision could technically apply to certain instances of this kind of sexting as well as to unauthorized redistribution to others. The technical applicability of the provision to consensual one-to-one sexts may be unevenly borne by girls who already appear both to be more likely than boys to send a sexualized self-representation and to suffer negative social consequences as a result. Prosecutors should not com- pound the negative social consequences already disproportionately borne by girls by criminalizing them for one-to-one sexts with intimate partners who were naively trusted to maintain their confidentiality. Nor should legal authorities hesitate to pursue unauthorized redistributions by former intimates that do engage the child pornography provision’s underlying objectives.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".