International and Comparative Best Practice in Education-Based Incarceration
Bibliographic record
Abstract
This chapter provides a systematic analysis of the most demonstrably successful correctional education programs presently in use in a number of countries worldwide. The discussion encompasses the following: (a) needs assessment instruments and their applications; (b) how governments in other countries adapt their educational offerings to the constrictions inherent in a jail setting, that is, making the most of a short stay; (c) the role of cognitive behavioral therapy (CBT) and related techniques in brief-term applications; and (d) new and innovative educational approaches showing promise. The comparative nature of this international survey reveals a high degree of similarity and a number of common features of successful programs, whether pursued in Canada, Germany, Italy, South Korea, Switzerland, the United Kingdom, or elsewhere. Our investigation of international best practices also highlights the obstacles, and opportunities, that implementing such programs in the American context might face. Finally, the chapter reviews the various methods by which successful outcomes are measured, including recidivism reduction figures and how they are defined; the cost savings of apprehending, prosecuting, and incarcerating offenders when it is shown that certain crimes did not occur due to the intervention of correctional education; and the measurement of improvements in the quality of life in disparate communities internationally.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".