Enhancing Offender Re-Entry an Integrated Model for Enhancing Offender Re-Entry
Bibliographic record
Abstract
Notwithstanding diminishing crime rates in many countries, high rates of incarceration continue to engage political and public scrutiny in the management of (increasing) correctional populations. It appears such interest is driven by the competing concerns of fiscal pressures and ideological shifts: essentially lack of funds and, in many jurisdictions, a lean towards more conservative doctrines regarding offender care. Perhaps not completely surprisingly, these two themes can also actually work in harmony. Nowhere is this more apparent than in the United States, where spiralling costs of corrections appear to have seduced politicians to consider less punitive models which, ironically, are more effective at reducing crime and costs. At present, various states have embraced what has often been referred to as the Canadian model, that is, a less punitive and more empirically-grounded rehabilitative approach to addressing crime. Indeed, the number of U.S. citizens involved in the criminal justice system is staggering (7.3 million adults), with approximately 700,000 individuals returning home each year to their communities from prison. Encouragingly, recent legislation and funding such as the Second Chance Act have put a spotlight on offender re-entry. The purpose of this paper is to critically examine how well the field is positioned to meet proffered expectations for re-entry regarding risk reduction and public safety and to debate whether existing conceptualizations of offender change can adequately inform offender re-entry initiatives.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".