Nigeria: In Search of Sustainable Peace in the Niger Delta through the Amnesty Programme
Bibliographic record
Abstract
Environmental pollution by way of oil spillage and gas flaring are the lots and bane of the Niger Delta region of Nigeria, where the country’s oil exploration activities are carried on by the oil multinational companies (MNCs). The cries of the people as well as several non-governmental organizations for attention to the area were not only spurned, but were at intervals rebuffed with crackdown and repression from successive administrations in the country, with the strong connivance of the oil MNCs. The situation reached a crescendo, when the people of this region took to self-help by bombing, kidnapping and abducting the expatriates and other categories of personnel of the oil MNCs in exchange for monetary ransom. The government not able to bear the embarrassment and the drop in daily oil production, coupled with the substantial loss of revenues devised the amnesty programme in 2009 as solution to the quagmire. The paper is aimed at examining the circumstances causing the crisis situation in the area, and the attendant consequences to the people of the areas and to the global community. It will attempt a critical analysis of the amnesty programme of the Federal government as a last resort and its impact at ensuring durable peace and sustainable development in the region. It discusses some of the challenges to amnesty programme and concludes with potential policy recommendations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".