Sustainable Development in Ghana's Petroleum Industry: An Analysis of the Resource Curse
Bibliographic record
Abstract
According to the ‘resource curse,’ countries with large endowments of natural resources perform worse than countries who are less endowed. So while Ghana’s recent oil discovery presents tremendous opportunities to assist in poverty alleviation, this so called curse has been unfortunately attributed to economic decline, democratic breakdown, environmental degradation, and civil unrest. Therefore this thesis seeks to evaluate Ghana’s preparedness in dealing with effects of new oil wealth and the impacts of oil exploitation on its environment and society. Interviews with members of civil society organisations, NGOs and government personnel revealed tremendous deficits and constraints in environmental protection, the rule of law, and political will; all of which will be further challenged by the onset of oil development in Ghana. Observations from interviewees, as well as the findings of contextual research provide the foundation for the goal of the research, which is to understand how prepared Ghana is to manage its future petroleum industry so that it encompasses environmental stewardship, economic development and social responsibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 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".