Bibliographic record
Abstract
Child pornography on the Internet is a problem almost invisible to the general public, which has produced consternation among those entrusted with the protection of society. The argument is presented that a great deal needs to be done to protect children and eradicate this illegal material from the Internet. I interviewed police officers, members of the judiciary, legislators and their advisors to discover what is being done to resolve these issues in Canada. The Canadian Criminal Code has been amended to include sections which deal specifically with child pornography and the Internet; sections 163.1(1) to 163.1(7) have proven to be inadequate in the face of challenges from the Canadian Charter of Rights and Freedoms particularly in the Sharpe case. The lack of legislative clarity begs inquiry into the formation of laws, their interpretation by the bench and enforcement by the police. In conclusion I present a series of recommendations, drawn from my interviews and from the available literature, to correct the problem of child pornography on the Internet and, thereby, to protect the children
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".