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Record W2570289392 · doi:10.1177/0975087816674577

Promoting Security in Africa through Regional Economic Communities (RECs) and the African Union’s African Peace and Security Architecture (APSA)

2017· article· en· W2570289392 on OpenAlexaff
Peter Arthur

Bibliographic record

VenueInsight on Africa · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPoliticsPolitical scienceEconomic growthEnterprise information security architecturePromotion (chess)Economic securityHuman securitySecurity studiesSecurity communityDevelopment economicsPublic administrationInternational tradeEconomicsLawComputer security

Abstract

fetched live from OpenAlex

The last two decades have seen African countries adopt a new security approach through the activities of regional economic communities (RECs) and the African Union’s (AU) African Peace and Security Architecture (APSA). This article argues that despite progress in conflict prevention and the promotion of peace, defence and security through the APSA and RECs, challenges do remain. In particular, factors such as financial costs involved, the inadequate funds available for peace and security missions, conflicting interests and lack of agreement, poor co- ordination and inadequate human and logistics capacity have constrained the ability of African countries to achieve their peace and security agenda. Thus, for peace and security efforts being undertaken by the AU and RECs to be effective, the actors involved should have not only the requisite capacity but also political will and commitment, and cooperation among members and with the international community should remain crucial to the process.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0000.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.282
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations24
Published2017
Admission routes1
Has abstractyes

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