MétaCan
Menu
← Back to cohort
Record W3037676033 · doi:10.22215/etd/2020-14014

To Breach or Not to Breach: Exploring Inconsistencies in the Interpretation, Enforcement, and Impact of Canada's Section 161 Order for Sexual Offending

2020· dissertation· en· W3037676033 on OpenAlexaboutno aff
Natasha Knack

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsRecidivismTerminologyCriminologyInterpretation (philosophy)Order (exchange)PsychologySection (typography)EnforcementPopulationLaw enforcementLawPolitical scienceSocial psychologySociologyBusinessComputer scienceDemography

Abstract

fetched live from OpenAlex

Community management strategies for people convicted of sexual offences (PCSOs) can hinder reintegration, which plays an important role in reducing recidivism. Section 161 of the Criminal Code is a prohibition order given to people convicted of sexual offences against children (PCSOCs) upon their release into the community. This study was the first to evaluate the 161 Order and explored inconsistencies in the interpretation and enforcement of these conditions among people subject to a 161 Order, community members, and undergraduates. Attitudes toward the treatment of PCSOs were found to mediate the relationship between group membership and subjective legal decision-making. Support for the conditions did not appear to moderate this relationship. Conditions most likely to be inconsistently enforced were also most commonly reported as impacting reintegration. Results suggest the need for more concrete terminology, and a re-evaluation of using long-term management strategies in a population with one of the lowest recidivism rates.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.079
GPT teacher head0.368
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicPsychopathy, Forensic Psychiatry, Sexual Offending→French-language works237,207→