Bibliographic record
Abstract
This chapter provides an overview of how mainstream criminology and criminal justice reached the conclusion that the literature on the effectiveness of prevention and correctional programming supported a “nothing works” position. It describes recognition of the value of human service in justice contexts (that is, the debate moved toward a “what works” position). The chapter discusses “what works and what does not work” from the perspective of different theoretical accounts of criminal behavior. The justice contexts in which treatment is provided include community and institutional corrections, as well as the young offender and adult systems. The justice context most often involves imposition of some type of judicial sanction. The chapter deals with “rehabilitation,” “reintegration” or “correctional treatment,” and reduced recidivism. The purposes of judicial sanctioning include retribution and/or restoration. Retributive justice is concerned with doing harm to offenders. Restorative approaches seek justice through efforts to repair harm done to the victim, to restore the community that may have been offended or disrupted by the criminal act, and to hold the offender accountable. Specific deterrence is intended to contribute to reduced recidivism. Finally, the chapter summarizes the meta-analytic evidence in regard to the effectiveness of adherence with the risk-need-responsivity model.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".