Preventing The Oil “Resource Curse” In Ghana: Lessons From Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ghana joined the list of oil-producing countries with the export of its first oil from the Jubilee oilfield in January 2011. President John Atta Mills's statement drawing attention to the potential paradigm shift as well as risks that the discovery of oil and gas imposes not only speaks to the complexity of extractive-industry-engendered development, but it also makes it imperative that the country learns from other countries’ successes and failures. In this article, we use the “resource curse” thesis to examine the emerging dynamics and complexities in Ghana's oil industry, with the attempt to draw both correlations with and lessons from the Nigerian case. The article highlights five key lessons in Nigeria's management of its oil–gas resources related to the legal-regulatory framework, development of the oil-producing areas, Corporate Social Responsibility, management of oil revenues, and oil–civil society nexus that Ghana should give serious thoughts to in order to leverage its new found oil wealth and avert the “resource curse.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 0.001 |
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 it