The Self-Regulation Model of Sexual Offending: The Relationship Between Offence Pathways and Static and Dynamic Sexual Offence Risk
Bibliographic record
Abstract
T. Ward and S. M. Hudson (1998) have proposed a self-regulation model of the offence process which is specific to sexual offenders and which attempts to account for the deficiencies in the traditional relapse prevention model as applied to this group of offenders. The self-regulation model is a nine-stage process of offending that addresses both the individual's goals with respect to the offending behavior (approach versus avoidance) and the manner in which the individual attempts to achieve these goals (passive versus active), resulting in four hypothesized pathways that lead to sexual offending. The present study evaluated the validity of this model with a sample of adult male sexual offenders (N = 80) treated within the Correctional Service of Canada. Results demonstrated support for the self-regulation model. Specifically, it was found that the four pathways contained in this model were differentially associated with offender types (e.g., incest offender, rapist, extrafamilial child molester, etc.). In addition, static and dynamic risk factors were found to vary among the four pathways in predicted directions and are consistent with the theoretical model. Finally, static and dynamic risk factors differentially predicted pathway membership, again in the expected directions. Implications of findings and the self-regulation model for the assessment and treatment of sexual offenders are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".