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Record W2790650766 · doi:10.5539/ilr.v7n1p213

Proposals for Equitable Governance and Management of Natural Resources in Nigeria

2018· article· en· W2790650766 on OpenAlexvenueno aff
Z. Adangor

Bibliographic record

VenueInternational Law Research · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsNatural resourceCorporate governanceFederalismEthnic groupGovernment (linguistics)Resource (disambiguation)Economic growthPolitical scienceDevelopment economicsBusinessPublic administrationEconomicsLawFinance

Abstract

fetched live from OpenAlex

The current regime of centralized natural resource governance poses one of the greatest threats to the stability of the Federation of Nigeria. The centralization of natural resource ownership and government is perceived by the ethnic minorities of the oil-producing Niger Delta Region of Nigeria as a tool of ethnic domination by the majority ethnic groups. Given the centrality of natural resources to the growth of Nigeria’s economy and the desirability of maintaining a stable federation, this research seeks to propose an equitable regime of natural resource governance that recognises and accommodates both national and regional interest in Nigeria’s abundant natural resources and thereby strengthens federal stability. This paper which adopts analytical and comparative research methodologies, argues that the current regime of natural resource governance in Nigeria is divisive and that only the participation of the federating states in the governance of natural resources exploited within their respective geographic boundaries would conduce to peace and inter-regional harmony and enhance the capacities of the federating states to develop at their varying speed according to the dreams of the Founding Fathers of Nigerian federalism. The paper concludes by recommending resource federalism whereby competence over natural resource governance could be shared between the federal government and the federating states.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.277

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 studies0.0000.000
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 head0.065
GPT teacher head0.332
Teacher spread0.267 · 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 designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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