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Record W1503676212 · doi:10.5787/43-1-1108

SECURITY EDUCATION IN AFRICA: PATTERNS AND PROSPECTS

2015· article· en· W1503676212 on OpenAlexaff
David M. Last, David Emelifeonwu, Louis Osemwegie

Bibliographic record

VenueScientia Militaria South African Journal of Military Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsIncentiveMultinational corporationCorporate governanceEconomic growthPolitical scienceInternational securityNational securityFood securityTraining (meteorology)Public relationsBusinessPublic administrationGeographyEconomicsAgriculture

Abstract

fetched live from OpenAlex

This article is part of a larger study exploring global patterns of security education, in order to enhance the collaborative pursuit of security by the majority of the world’s countries. We draw on interviews at multinational training events, site visits and open sources. Here we describe general patterns of police, gendarme and military education in Africa, with particular attention to university-like institutions. This leads us to focus on mid-career military staff colleges as the most likely venues for building communities of educated professionals to enhance security. We identify states in each region with the greatest potential to play a leading role in the development of knowledge addressing new security challenges. South Africa, Nigeria and Kenya have obvious educational potential. Good governance and national policies are more important than size and wealth, and this suggests that smaller states like Senegal and Botswana could make important contributions. Mechanisms contributing to regional security communities include the African Peace and Security Architecture, career incentives, innovation, and regional training centres. Understanding the patterns of security education lays the groundwork to understand innovation, diffusion and the influence of the content of security of education.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.322
Teacher spread0.281 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScientia Militaria South African Journal of Military StudiesSame topicPeacebuilding and International SecurityFrench-language works237,207