Instrumental and Political Explanations in the Implementation of Various Prison Public-Private Partnership (PPP) Models
Bibliographic record
Abstract
This study highlights that the practice of Public-Private Partnership (PPP) in prison, based on the experiences of various countries such as the United States, England, Australia, France, Japan, and Canada, does not have a single pattern. The diversity of PPP practice patterns in prison is identified by this study in various prison PPP models consisting of: 1) the "private governance" model; 2) the "hybrid management" model; 3) the "service infrastructure" model; and 4) the "contracting out certain limited functions" model. Through a literature study combined with an analysis of policy design theory, this study explains that the diversity of prison PPP models is based on a diverse implementation process in which there are various considerations for the implementation of each model. The various considerations for the implementation of those models are generally grouped into instrumental and political explanations. The implementation of the "private governance" model is a political and administrative decision. The implementation of the "hybrid management" and "service infrastructure" models are administrative decisions with political support. The implementation of the "contracting out certain limited functions" model is an administrative decision.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".