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Record W3095192811 · doi:10.1163/17087384-12340068

National Security, Property Rights, and Development in Nigeria: How Should the Leviathan Resolve Herder-Farmer Conflict?

2020· article· en· W3095192811 on OpenAlexvenueno aff
Chidebe Matthew Nwankwo

Bibliographic record

VenueAfrican Journal of Legal Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsAgrarian societyProperty rightsState (computer science)PoliticsInsurgencyPolitical sciencePolitical economyEthnic conflictEthnic groupPastoralismDevelopment economicsLawSociologyGeographyEconomics

Abstract

fetched live from OpenAlex

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.106
GPT teacher head0.316
Teacher spread0.209 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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