Bibliographic record
Abstract
1. Sex Offenders: Policy and Legislative Developments. Hazel Kemshall, De Montfort University and Gill McIvor, University of Stirling. Part One: The Characteristics of Sexual Offenders. 2. Adult Male Sex Offenders. Dawn D. Fisher, Llanarth Court Psychiatric Hospital and Anthony R. Beech, University of Birmingham. 3. Female Sex Offenders. Hazel Kemshall. 4. Young Sex Offenders. Helen Masson, University of Huddersfield. Part Two: Assessment and Effective Interventions. 5. Risk Assessment of Sex Offenders. Don Grubin, St Nicholas Hospital, Newcastle. 6. Effective Intervention with Sexual Offenders. Bill Marshall, Gerris Serran and Heather Moulden, Rockwood Psychological Services, Kingston, Ontario, Canada. 7. Treatment of Sex Offenders in the UK in Prison and Probation Settings. Anthony R. Beech and Dawn D. Fisher. 8. Managing Children and Young People Who are Sexually Aggressive. Andy Kendrick, University of Strathclyde. 9. Relapse Prevention: Theory and Practice. Tony Ward, Victoria University of Wellington, Mayumi Purvis, University of Melbourne and Grant Devilly, Swinburne University, Australia. Part Three: Community-based Risk Management Strategies. 10. Multi-Agency Public Protection Arrangements: Key Issues. Mike Maguire, Cardiff University and Hazel Kemshall. 11. Sex Offender Registers and Monitoring. Terry Thomas, Leeds Metropolitan University. The Contributors. Subject Index. Author Index.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".