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Record W2161367543 · doi:10.5539/jpl.v7n4p176

Politics of Resource Control and Revenue Allocation: Implications for the Sustenance of Democracy in Nigeria

2014· article· en· W2161367543 on OpenAlexvenueno aff
Mukhtar Abdullahi

Bibliographic record

VenueJournal of Politics and Law · 2014
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSustenanceDemocracyRevenue sharingPoliticsPovertyUnemploymentEconomic growthIncentiveRevenueDevelopment economicsScope (computer science)Political scienceBusinessEconomicsLawFinanceMarket economy

Abstract

fetched live from OpenAlex

The agitation for the control of resources by the oil producing states in the Niger-Delta area is one of the major challenges confronting the nation and our nascent democracy. This controversial issue has re-surfaced during the recently concluded National Conference held between April and August 2014. The delegates from the oil producing states (south-south) demanded for between 25.5% to 50% derivation funding, which was not approve by the conference members. This paper analyses the nature and scope of this agitation and its implications for the sustenance of democratic federal system in Nigeria. The problem of mass poverty and unemployment among youths in Nigeria coupled with collapse of infrastructure, environmental degradation and pauperization of land in the Niger - Delta due to the activities of oil companies were the factors responsible for the trend. The main thrust of the recommendations hinged on ensuring a reasonable and fair sharing formula for the country’s resources, and making adequate compensation to the people of the area through infrastructural development by both governments and the participating oil companies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.025
Scholarly communication0.0140.007
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2014
Admission routes1
Has abstractyes

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