Urgent issues and prospects in correctional rehabilitation practice and research
Bibliographic record
Abstract
Abstract The aim of this paper is to identify some of the urgent issues currently confronting criminal justice policymakers, researchers and practitioners. To this end a diverse group of researchers and clinicians have collaborated to identify pressing concerns in the field and to make some suggestions about how to proceed in the future. The authors represent individuals with varying combinations of criminal justice research, professional training (e.g. social work, criminal justice, criminology, social work, clinical psychology) and clinical orientation, and experience. The paper is comprised of 13 commentaries and a subsequent discussion based on these reflections. The commentaries are divided into the categories of explanation of criminal behaviour, clinical assessment and correctional intervention, and cover issues ranging from the role of clinical expertise in treatment, problems with risk assessment to the adverse effects of social oppression on minority groups. Following the commentaries, we summarize some of their key themes and briefly discuss a number of major issues likely to confront the field in the next 5–10 years.
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 | 0.216 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.042 |
| Scholarly communication | 0.033 | 0.037 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.030 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".