Proposals for Equitable Governance and Management of Natural Resources in Nigeria
Bibliographic record
Abstract
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".