Using a Collaborative Approach with Criminal Justice Clients: A Promising Narrative in Rehabilitation
Bibliographic record
Abstract
After working with several correctional-based treatment programs the past several years, the authors have encountered several counterproductive narratives such as: rehabilitation does not work, expectations of the criminal justice system for therapists, monolithic stereotypes of criminal justice clients, and the belief that there is a preferred way to conduct therapy with criminal justice clients. Unfortunately, these themes tend to cultivate an “us versus them” split and complicate therapeutic efforts. To enhance their work with the clients, the authors explored the use of postmodern therapy ideas and found these helpful. In particular, the use of the ideas from the collaborative languaging systems approach, with notions such as not-knowing, collaboration, the client as expert, and problem dissolution redefined how the authors view effective therapy with this population.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.039 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".