The implications of oil theft on social and economic development in the Niger Delta
Bibliographic record
Abstract
The emergence of the exploration of crude oil in the Niger Delta area of Nigeria, has awarded the region worldwide renown as the economic backbone of the country, but also as a conflict flashpoint. Drawing from the propositions of the resource curse theory, the paper identifies Nigeria’s rentier state structure as the underlining cause linked to the citing of conflict and corruption, as the reasons for the occurrence of oil theft in the Niger Delta. Also, the Dutch disease is identified as an economic explanation of the resource curse theory, and this is used to identify the economic implications of oil theft in the Niger Delta at the national level. In addition, the rentier state structure is used to identify the social implications of the occurrence of oil theft at the local level in the Niger Delta region. The paper posits that economic implications include reduced revenue, increased unemployment, and diversification of the economy. The social implications also include sustained conflict, curbed social development, and displacement of persons. To combat the illegal practice of oil theft, it is recommended that transparency and accountability should be adhered to in the relations among government, oil-producing communities and multinational corporations. Keywords: Niger Delta, oil theft, resource curse, sustainable development, security and conflict
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".