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Record W3217748340 · doi:10.5539/ass.v17n12p1

Cooperation Between the Countries Around Lake Chad Basin: An Assessment

2021· article· en· W3217748340 on OpenAlexvenueno aff
Safiya Wada Abu, Adam Okene Ahmed

Bibliographic record

VenueAsian Social Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionOperationalizationStructural basinAccountabilityWork (physics)Political sciencePoliticsGovernment (linguistics)Resource (disambiguation)GeographyEnvironmental resource managementEnvironmental planningGeologyEconomicsLawEngineering

Abstract

fetched live from OpenAlex

The Lake Chad Basin is an important natural resource that cut across several countries among which are Nigeria, Niger, Chad, Cameroon, Algeria, Central African Republic and Libya. In a bid to ensure the effective utilization of the water of the basin, the countries have engaged in cooperation through the creation of Lake Chad Basin Commission. The Commission has embarked on certain programmes to achieve its aim, hence the need for the assessment of the cooperation between countries around the basin. This work is an assessment albeit critical, of the cooperation within that commission. Part of the findings of the paper is that the Lake Chad Basin Commission has been unable to achieve objectives it sets for itself. Certain challenges which include but not limited to, lack of political will among members of the Commission, reoccurrence of violence, lack of adequate finance, poor organizational structure, cultural and language difference have worked either individually or in tandem to frustrate the realization of what appeared ab initio to be the noble and lofty goals of the commission. The contention of the paper therefore, is that the Lake Chad Basin Commission member states should reflect and modernize its initial objectives and operationalize the ingredients of its cooperation to derive the positivity laden in the agreements or else risk the extinction of an important water resource. Data for the paper were sourced using both primary and secondary. Other variables and methodological approaches like analysis, discourse, and accountability and of course, chronological delineations were generously employed in reconstruction. Study of this nature is multidisciplinary and knitted in the International studies, Security studies, and Diplomatic and Military history.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.349
Teacher spread0.323 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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