Perspectives on the capacity of the Canadian police system to respond to "child pornography" on the internet
Bibliographic record
Abstract
The Internet, its affordability, accessibility, and anonymity provide new venues where\nchild exploitation crimes have increased. An exponential rise in the exchange of images of\nsexual abuse, commonly referred to as ‘child pornography’, has occurred. The purpose of\nthis major paper was to explore this phenomenon within an international context, and\nassess the capacity of Canadian law enforcement (national and municipal) to respond. In\norder to do so a survey was sent to police departments across Canada, to have officers\nidentify the challenges they faced in responding to images of child abuse on the Internet,\nand to solicit officers’ general opinions on this issue. The research resulted in five key\nfindings that implied that existing capacity gaps were rooted in a lack of applied or\nratified international agreements and commitments, a failure of system interoperability,\na lack of effective private-public partnerships, and the weaknesses in current Canadian\nlegislation, particular to mandated reporting of suspicious content (which is now under\nreview). Finally, a lack of appropriate, accessible support and training for police was\nidentified. Informed by the research, the author makes several recommendations.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".