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Record W4378908383 · doi:10.5539/res.v15n2p34

Application of Conflict Wheel Model in the Analysis of Farmers-Herders Conflict in Adamawa State, Nigeria

2023· article· en· W4378908383 on OpenAlexvenueno aff
Mustapha Salihu, Chigozie Enwere

Bibliographic record

VenueReview of European Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsHerdingContext (archaeology)DesertificationGovernment (linguistics)State (computer science)PoliticsCompetition (biology)Conflict resolutionScope (computer science)Political scienceDevelopment economicsPolitical economySociologyEconomicsEcologyGeographyLaw

Abstract

fetched live from OpenAlex

The study examines the causal factors of the lingering conflict between farmers-herders in the Adamawa. Specifically, it emphasizes issues, dynamics, context, causalities and exit options. The application of the conflict wheel theory suggests, the negative consequences of desertification and decline in ecological resources has in this case informed an unhealthy competition for access to mutual ecological resources between farmers-herders as one of the factors that brought about the deadly conflict. Further analysis reveals the context which has consistently increased in scope, cannot be divorced from existential socio-economic, political, and cultural realities of the Nigerian state. While Causalities seem to be multidimensional encompassing, conflict between two production systems, government inactivity and pre-existing security challenges in the region amongst others discussed. Given the central role of the state in enforcing law and social stability, it is imperative that government at various levels review pre-existing strategies and adopt robust and inclusive strategies. Similarly, the importance of counter desertification and conservative policies cannot be downplayed, and climate conscious pastoral and herding initiatives will also go a long way in averting future ecological resource-based conflicts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.315
Teacher spread0.273 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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