Climate Change, Environment and Armed Conflicts in Nigeria
Bibliographic record
Abstract
Climate change has become a major cause of conflicts in Nigeria, which directly causes multiple forms of insecurity in the country. In different parts of the globe, it manifests as earth quake, hurricane, tsunami, etc. Nigeria has received its share of climate change both in two opposite forms. In the southern coastal states of Lagos, Bayelsa, and Rivers State, the ocean and overflowing waters continually threatens to wipe away the people. However, this study focuses on the north and parts of southern Nigeria, where the impact of climate change has generated armed conflict. The study which used qualitative methodology traced how climate change and the emergence of drought, famine and other forms of environmental changes leads to resource competition over land, mineral resource, water ways and by extension generating armed conflicts in many parts of Nigeria. It found that climate change caused mass migration and the settler versus non-settler conflicts that manifested in different as herdsmen-farmer conflict, as well as the armed conflict among the Ezza and her neighbours and also contributed to the Ife-Modakeke crisis in the country. Finally, the study documents multi-dimensional road-map to environmental peace and adaptations for sustainable societal development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".