Confronting the Governance Challenges of Developing Nigeria's Extractive Industry: Policy and Performance in the Oil and Gas Sector<sup>1</sup>
Bibliographic record
Abstract
Abstract The issue of economic growth and human development has been a central concern in the oil and gas sector of Nigeria's extractive industry, and this has featured prominently in the agitations and generalized restiveness in the Niger Delta, the oil‐producing region. While many studies have focused on these problems, policies aimed at confronting them have not received much broad attention. This article bridges this research gap by holistically focusing on the solution to the problem. In doing this, the article examines the policies during the Obasanjo administration from 1999 to 2007 in order to critically assess the efficacy or inefficiency of the policies in reversing the general problem now known as the “resource curse,” and to offer a better understanding of the deeper political, social, and economic issues that drive outcomes. The article finds that while significant efforts were made to avoid the boom and bust cycle of oil and lower volatility by de‐linking public expenditure from oil revenue through the “oil‐price‐based fiscal rule,” generally, progress in this area was not matched by improvement in the other areas examined by this study, notably peace and safety of lives and oil/gas installations, the development of the oil‐producing region, environmental security and sustainability, and the transparent and accountable use of oil/gas revenues.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".