National Security, Property Rights, and Development in Nigeria: How Should the Leviathan Resolve Herder-Farmer Conflict?
Bibliographic record
Abstract
Abstract The spate of violent clashes between nomadic pastoralists and agrarian communities in Nigeria raises a number of legal and policy questions that had been long overlooked. Issues arising from the phenomenon range from questions over constitutionally guaranteed rights such as the right to own property, to questions over the inadequacies of Nigeria’s security apparatus as well as calls for land use reforms. Additionally, due to the groups affected and the scale of casualties, the topic has become a political molten magma. The constant conflicts between nomadic pastoralists who are majorly from the Fulani ethnic group, and agrarian communities from other parts of the country have reached unprecedented levels leading to accusations of coordinated attempts at land grab, ethnic cleansing, jihad and insurgency, threatening the country’s security and stability in the process. Fiscally, the destruction of lives and property and the state of insecurity emanating from the clashes stood at $16 billion in potential revenue as at 2018. In no small measure have these clashes been precipitated by climate change and the consequent drought in the Sahara region. This paper analyses the role of the Nigerian state in balancing the interests of affected groups in the clashes and promoting development. At its core, it seeks to identify legal and policy gaps that require filling to put a definite end to the lingering crisis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".