Examining the good lives model and antisocial behaviour
Bibliographic record
Abstract
This study examined the utility of the Good Lives Model (GLM) (Ward, T., & Stewart, C. A. (2003). The treatment of sex offenders: Risk management and good lives. Professional Psychology: Research and Practice, 34(4), 353–360. doi:10.1037/0735-7028.34.4.353) in understanding offending behaviour in students. Two hypotheses were made, consistent with the assumptions of the GLM. First, that participants would endorse the importance of the primary goods set out in the GLM. Second, that reports of antisocial behaviour would relate to a lack of effective strategies, or use of maladaptive strategies, to achieve primary goods. Participants (n = 340, M age = 20 years) completed a questionnaire (Measure of Life Priorities) assessing their pursuit, valuation, and achievement of the primary human goods as set out in the GLM and a Self-Report of Offending questionnaire. Results supported our hypotheses, and subsequently the assumptions of the GLM. Our findings support the continued use of the GLM as a theoretical and treatment oriented framework in diverse groups engaged in offending behaviour. Future research should continue to ground the GLM in empirical support.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".