Examining the Benefits and Challenges of the Diversion Programme as a Mechanism to Enhancing Juvenile Justice Administration in Dar es Salaam, Tanzania
Bibliographic record
Abstract
This study is informed by goal number four, target one of the Sustainable Development Goals (SDGs), on the increase of accessible, equitable, and quality primary and secondary education by 2030. The intended outcome of this goal cannot be achieved in Tanzania when juveniles who fall in conflict with the law are left out of the mainstream of education. The study utilized a cross-sectional design, which embraces the qualitative approach. Its data were obtained through purposeful sampling techniques (convenient and snowballing sampling), in which twenty-eighty respondents participated. The data collection techniques used were in-depth interviews and focus group discussions. The ATLAS.ti 9 software was employed during qualitative data analysis. The main findings reveal that the diversion programme effectively addresses juveniles' educational needs and mitigates associated stigma and retribution. Furthermore, it helps in amicably solving the juveniles' problem due to assessment of individual juveniles, generating suitable tailor-made interventions. Challenges obtained were that police officers still used much force during the arrest, and some would demand bribes; the traditional system was lengthy and cumbersome; there was a shortage of workforce and buildings; limited financial resources and equipment; and some juveniles and actors did not know the programme. The article concludes that the diversion programme is vital to achieving education for all as envisaged by the SDGs. It further recommends capacity building to all social actors on the importance of diversion, mobilization of resources, and researchers should be encouraged to conduct studies in JJA, in particular indigenous models and practice of the diversion programme.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".