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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".