Bibliographic record
Abstract
The Drug Treatment Court (DTC) model was \n conceived out of the need to solve the numerous and \n intractable problems that drug related cases create for \n court systems. A DTC is generally seen as a court that deals \n specifically with offenders who have committed offenses \n while under the influence of drugs and provides an \n alternative to incarceration. DTCs make use of a \n multidisciplinary team involving judges, prosecutors, \n defense attorneys, probation officers, treatment providers, \n police officers, and educational and vocational experts. The \n criminal justice and health service systems join to provide \n drug-dependent offenders with the mechanisms to recover from \n drug addiction and lead a productive and crime-free life. \n The purpose of this paper is to explore the concept of DTCs. \n After providing an overview of the origins of the DTC, \n looking at its roots in the United States and Canada, the \n paper examines the foundation and present-day experiences of \n DTCs in Jamaica. It also refers to some efforts among \n various countries in the Western Hemisphere to monitor DTCs \n and evaluate their effectiveness. The paper concludes with a \n return to the achievements of DTCs in Jamaica and a brief \n look at the future of the DTC program worldwide.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".