Contingency Management Programs in Corrections: Another Panacea?
Bibliographic record
Abstract
The mantra of best practices in corrections, while well intended, may lead to iatrogenic consequences. Community corrections and prisons are under increasing pressures to manage their caseloads; moreover, the current accountability and get-tough agenda in corrections demands offenders take on more responsibility for their behaviors. As a consequence, we predict more episodes of “panaceaphilia” or quick fix solutions because corrections jurisdictions in the United States are under tremendous pressure to handle their populations at this point in time. In this article, we focus on contingency management programs as the potential next panacea, not because they do not have a proven track record of success, but because they require highly skilled staff and make great demands upon correctional agencies’ decision-making practices. To help counteract panaceaphilia from happening with contingency management, we describe the theory and practice of contingency management, the demands they place on programmers, the type of research needed to evaluate their effectiveness, and how to prevent these programs from turning into punitive punishment regimes.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".