Misunderstandings and Intentional Misrepresentations: Challenging the Continued Framing of Consensual and Nonconsensual Intimate Image Distribution as Child Pornography
Bibliographic record
Abstract
Abstract Many educational presentations continue to straightforwardly frame both consensual and nonconsensual intimate image distribution among youth as child pornography. This continues despite the availability of a purpose-built offence for nonconsensual intimate image distribution (NCIID) that was designed, in part, to avoid the use of child pornography offences in NCIID cases and the existence of a “private use exception” that limits the applicability of child pornography offences in cases of consensual “sexting” among youth. This sometimes inaccurate and, I argue, inappropriate focus on child pornography offences is especially common in presentations by police and public safety personnel. Through a discursive analysis of Canadian case law and a case study of educational approaches provided by the CyberScan unit, I find that the continued dominance of a child pornography framing is based on both genuine misconceptions of how these offences apply to intimate image distribution and intentional misrepresentations of the legal context.
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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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.021 | 0.065 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".