MétaCan
Menu
Back to cohort

Riots/Jail Breaks in Nigeria Prisons: An Aetiological Study

2013· article· en· W2118894056 on OpenAlexvenueno aff
Don John Omale

Bibliographic record

VenueCanadian social science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonBoko haramCriminologyGovernment (linguistics)Port harcourtPolitical scienceLawInsurgencySociologyPoliticsSocioeconomics

Abstract

fetched live from OpenAlex

In recent times, riots/jail breaks in the Nigerian Prisons have become recurrent phenomena. So rampant, that they pose security concerns and serious threats not only to the prison authority, but also to both the government and the people of Nigeria. For instance, on the 2nd of January, 2013 about 20 inmates escaped from a secured prison in Sagamu, and on 15th February 2012 Boko Haram attacked Koto-Karfi prison in Kogi State, releasing about 119 Awaiting Trials Persons (including Boko Haram suspects). Other examples of riots/jailbreaks in the Nigerian Prisons include: The February 2004 riot in Ikoyi prison, the Port Harcourt prison attack of 2005 and Onitsha prison attack of the same year. On 6th September 2007 riots occurred in Kano prison and on 8th September 2007 riots occurred at Agodi prison in Ibadan. On Wednesday 3rd June, 2009 about 150 inmates broke jail at Enugu prison. On 20th of April, 2010 Kaduna prison experience jail break; and the Boko Haram attacked Bauchi and Maiduguri prisons in 2010 and 2011 respectively, to mention a few. This study investigates this phenomenon using an 18 items semi structured questionnaire administered to 240 inmates of Kaduna Central Prison, and a Focused Group Discussion (FGD) with 10 Deputy Controller of Prisons (DCP) to unravel the aetiology of prison riots/ jailbreaks in Nigeria. Key words: Prison and security; Jailbreaks; Nigerian prisons

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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.

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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207