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Record W3160575434 · doi:10.1787/30204d8f-en

Non-military actors as a regional strategy in the Lake Chad region

2021· report· en· W3160575434 on OpenAlexfundno aff
Olajumoke Ayandele

Bibliographic record

Venue˜The œWest African papers · 2021
Typereport
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
FundersYork University
KeywordsPolitical scienceTerrorismCommissionResilience (materials science)Member statesBorder SecurityGeographyEnvironmental planningPublic administrationEuropean unionBusinessInternational tradeLaw

Abstract

fetched live from OpenAlex

The purpose of this paper is to examine current regional strategies employed to counter extremism in the Lake Chad Basin region. Using the Lake Chad Basin Commission (LCBC) as a case study, the paper highlights the importance of non-military actors in shaping African regional military strategies. Regional peace and security frameworks have generally placed a predominant emphasis on member countries’ militaries and their institutions. Unfortunately, such an approach remains incomplete in effectively countering transnational terrorist threats. By assessing current LCBC collaborative mechanisms with non-military actors under the Regional Stabilisation Strategy created in 2018, the paper concludes that there is a need to incorporate more local actors in the regional security framework. Such collaborations will improve civil-military relations while boosting the resilience of member states in combatting Boko Haram and other transnational groups.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.049
GPT teacher head0.326
Teacher spread0.277 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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Same venue˜The œWest African papersSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207