Application of Conflict Wheel Model in the Analysis of Farmers-Herders Conflict in Adamawa State, Nigeria
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".