An examination of judicial sentencing decisions in child pornography and child molestation cases in Canada
Bibliographic record
Abstract
Purpose Accessing and distributing child pornography is an emerging problem. This paper aims to examine the judicial sentencing decisions of child pornography cases and whether they differ from decisions of child molestation cases. Design/methodology/approach Using a legal database of Canadian court judgments, the study examined sentencing decisions of 50 child pornography and 50 child molestation cases, identifying variables that were present in the judges' reasons for their decision. Findings The results revealed a disparity in sentencing that favours incarceration rather than community sentences for child molesters over child pornography cases. Despite what appears to be lighter sentences for child pornography offenders, judges were more likely to sanction treatment and recommend restrictions in cases of child pornography than child molestation. In light of the absence of literature exploring sentencing disparity among child sexual offences, further directions and suggestions for practice are discussed. Practical implications The examination of the disparity of sentencing decisions for child molesters and child pornography offenders and the identified variables that may contribute to these decisions suggests that the judiciary views child pornography and child molestation offenders differently and are more punitive toward contact offenders. Such disparity has implications for the criminal justice system. Originality/value This study offers the first exploration of sentencing disparity and decisions on child pornography and child molestation cases in Canada.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".