An Examination of the Socio-Economic and Environmental Impact of Planned Oil Sands Development in Nigeria
Bibliographic record
Abstract
Nigeria became a mono-product economy through its heavy reliance on crude oil for export and revenue. As a result of oil price volatility and the need to increase national revenue and public spending, the Nigerian government has adopted a policy of diversification to non-oil minerals. This study focuses on oil sands that are considered second only to oil in terms of economic potential. This policy also means that Nigeria is moving towards finite resource and a dirtier form of crude oil. A national analysis of non-oil mineral activity using a GIS indicates that exploration, mining and quarrying are widespread; a potentially positive outcome for national mineral development. The government however, is failing to take into account the impact of this activity on communities and ecosystems overlapping or lying proximal to mining licences. A case study indicates that oil sands exploitation can have a positive impact on the host communities through infrastructure development, which can trigger small businesses, job opportunities and increased income. Despite these benefits, there are fears of environmental degradation, displacements, loss of communal lands and means of livelihood. Already, the long delays in the development of oil sands are fuelling anger, deprivation, land grabs and pollution, and worst of all, ever-deeper underdevelopment of these „conditional resource communities’, which is aggravating the resource curse. For the oil sands projects to be feasible, beneficial and sustainable, Nigeria’s quest for resource wealth must integrate economic growth, social equity and ecological integrity at this planning stage. The thesis makes original contributions to determining resource communities and to the cumulative body of knowledge on the potential impacts of resource development on host communities in a rent-seeking economy like Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".