Bibliographic record
Abstract
Contents Preface 1 Setting some boundaries: rethinking responses to sex offenders (Amanda Matravers, University of Cambridge) 2 Serious sexual assault: using history and statistics (Keith Soothill, Lancaster University) 3 The legislative framework (Cathy Cobley, Cardiff University) 4 Disclosing information on sex offenders: the human rights implications (Helen Power, University of Glamorgan) 5 Sex offenders, risk penality and the problem of disclosure to the community (Hazel Kemshall, De Montfort University, and Mike Maguire, Cardiff University) 6 Interpreting treatment performance of sex offenders (Michael Seto, Center for Addiction and Mental Health, Toronto) 7 The Machiavellian sex offender (David Thornton, Sand Ridge Treatment Center, Mauston, Wisconsin) 8 The role of the polygraph (Don Grubin, Royal Victoria Hospital, Newcastle-upon-Tyne) 9 Adolescents who sexually abuse (Rowland Coombes) 10 Developing multi-agency public protection arrangements (Tim Bryan and Paddy Doyle, National Probation Directorate) 11 Joined-up worrying: the Multi-Agency Public Protection Panels (Roxanne Lieb, Washington State Institute for Public Policy, USA) 12 Challenges for the police service (Terence Grange, Chief Constable, Dyfed Powys Police) 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".