Criminal Justice System Capacity Building: Lessons from a Longitudinal Training Project in Guyana
Bibliographic record
Abstract
By drawing on two data sets— a performance monitoring plan and an outcome-based evaluation— generated over five years, this article describes training practices developed within a criminal justice system capacity building project in Guyana. The key stakeholders the project included members of the police force, including crime scene and police investigators, police prosecutors and public prosecutors, staff of the forensic labs, magistrates, and judges. The training sessions were led by international subject matter experts in a multidisciplinary and cross-sectional environment. Analysis of the data with reference to program’s guiding educational principles, reveals the following positive factors of the trainings: recognition of co-constructed knowledge within a learning community, cross-sector training, and ongoing workplace support. The article showcases some of training practices and offers strategies for further development of high-impact educational programs for criminal justice system. The authors argue that such training programs need to be dynamic, collaborative, responsive, iterative, and embedded in the enabling environment of a community of practice.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".