Oil Pipelines Vandalism and Oil Theft: Security Threat to Nigerian Economy and Environment
Bibliographic record
Abstract
Nigeria is a middle income country whose economy depends largely on crude and refined oil from its natural environment. A larger percentage of Nigeria economy survives mainly on the incomes from oil production. Over the years, there is recurrent dwindling oil revenue orchestrated by oil pipelines vandalism and oil theft in the environment. This is predominant in the Niger Delta Region of Nigeria. This menace has wreaked havoc on the Nigeria’s economy. Currently, the Nigerian National Petroleum Company Limited (NNPCL) claims the losses of 470,000 barrels per day of crude oil amounting to $700 million monthly due to oil theft. The disquiets of these menaces in the environment, which have posed serious threat to Nigeria’s economy, are addressed in this paper. This paper employed the doctrinal legal research methodology in evaluating the recurrent oil pipelines vandalism and oil theft causing a devastating economic meltdown. On this premise, this paper finds that persistent loss of barrels of crude oil and degradation of the environment are due to the lack of adequate security measures and proper enforcement of Oil Pipelines Act together with other relevant environmental laws. Based on the findings, this paper recommends a review of the Oil Pipelines Act, the establishment of a strong environmental security surveillance, and creation of a special court for accelerated prosecution of vandals. It concludes that this will mitigate the alarming economic meltdown of the Nigeria’s economy and promote a sustainable serene environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".