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Record W2990874953 · doi:10.3968/11297

Challenges to Effective Implementation of Rehabilitation Programmes for Prison Inmates in Southwestern Nigeria: An Empirical Approach

2019· article· en· W2990874953 on OpenAlexvenueno aff
Isaiah Oluwaseyi Alamu, Wasiu Abiodun Makinde

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Sociopolitical Dynamics in Nigeria
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonRehabilitationGovernment (linguistics)SustainabilityPolitical sciencePublic relationsWelfareThe InternetBusinessPublic administrationMedicineMedical educationLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.396
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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