Bibliographic record
Abstract
The author challenges the assumption that the expansion of child pornography offenses can lead only to a decrease in harm to children and to society. He argues that Canadian child pornography law is incoherent. In some respects, child pornography law makes valuable contributions to the prevention of child sexual abuse by targeting the production, dissemination and use of material ("real" child pornography) that involved harm in production. It also improves the law by criminalizing written and visual material that advocates the commission of sexual crimes against children and youth. In other respects, the law causes harm to society by suppressing thoughts and expression concerning child and youth sexuality that involved no harm in production, fall short of advocating harm and that have at best a tenuous connection to the commission of harmful acts. The Canadian child pornography offense criminalizes a range of creative expression in the absence of any persuasive evidence of a risk of harm. Amendments to the offense since the Supreme Court of Canada ruling in Sharpe (2001) have exacerbated its impact on civil liberties. A fundamental reconsideration of the design and scope of the child pornography offense is required to ensure it is focused on achieving its objectives in a constitutionally sound manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".