Challenges to Effective Implementation of Rehabilitation Programmes for Prison Inmates in Southwestern Nigeria: An Empirical Approach
Bibliographic record
Abstract
The agitation for rehabilitation of prison inmates in Nigeria, especially in the southwest, have been a major discourse in the purview of professionals such as administrators, academics, policy makers and actors. This has propelled governments in collaboration with Civil Society organisations and a few religious bodies to investigate and implement programmes to better the lots of inmates by providing resources and desirable environment for its sustainability. Regrettably, these contributions have yielded meagre outcomes as a result of challenges encountered during implementation. Consequent upon this, this paper investigated the challenges of rehabilitation programmes for prison inmates in Southwestern Nigeria. Primary and secondary data were utilised through administration of questionnaire and indebt interview. Secondary data were obtained from text books, documents and internet. The paper revealed that delay in court procedures on awaiting trial inmates, lack of funds for rehabilitation programmes by the prison administrators and poor inmates’ welfare are major challenges to the effective implementation of rehabilitation programmes for inmates. Therefore, the study recommended that the Federal Government should provide an enabling environment for rehabilitation of inmates, as well as make more funds available for its continuous sustainability.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".