A 'Reasoned Apprehension' of Overbreadth: An Alternative Approach to the Problems Presented by S.163.1 of the Criminal Code
Bibliographic record
Abstract
In this Note, I explore the theoretical problems and paradoxes created by the breadth of the child pornography offences. I am not suggesting that the moral core of the legislation is ill-advised; in fact, there is ample justification for the offences of simple possession and accessing of real child sex abuse images online, as well as for mandatory minimum sentences. However, it makes little sense to extend s.163.1 beyond real child sex abuse images to subject fictional representations, which bear little or no resemblance to actual child sex abuse images, to the same highly punitive and stigmatizing regime as images depicting the sexual victimization of real people. From a practical perspective, it distracts us from the urgency of combating the circulation of real child sex abuse images on the Internet and it places the legislation at constitutional risk. The Note is divided into five parts. Following this introduction, Part II will review the connection between child pornography and freedom of expression in Canada. Part III traces the history and development of the child pornography provisions. Part IV considers the various problems with s.163.1 and suggests how the legislation can be re-drafted to save it from constitutional overbreadth. Part V sets out a new model for child pornography legislation in Canada. This Note concludes with a brief summary, followed by my recommended changes to the legislation, in the Appendix.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".