Exploring Inconsistencies in the Interpretation of Canada’s Section 161 Order for Sexual Offending
Bibliographic record
Abstract
Community-based risk management strategies for people convicted of sexual offences (PCSO) can hinder successful reintegration, which plays an important role in reducing sexual recidivism. Section 161 of the Criminal Code is a Canadian risk management strategy, which aims to protect children by prohibiting people convicted of sexual offences against children (PCSO-C) from engaging in behaviours assumed (sometimes erroneously) to be associated with sexual offending. This study was the first to evaluate Section 161 prohibition orders. We explored inconsistencies in the interpretation and (hypothetical) application of these conditions between PCSO-C subject to Section 161 and two non-forensic subsamples of the Canadian public – community members and undergraduate students. Non-forensic participants expressed more negative attitudes towards the treatment of PCSO, which were found to mediate the relationship between group membership and subjective legal decision-making. Degree of support for Section 161 conditions did not appear to moderate this effect. Results raise concerns about the potential for increased personal discretion when enforcing or adhering to ambiguous or overly broad legal conditions. We suggest the need for continued efforts to establish an empirical understanding of the application, efficacy, and potential collateral consequences associated with this Canadian risk management strategy.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".