Transparency in Resource Governance: The Pitfalls and Potential of “New Oil” in Sub-Saharan Africa
Bibliographic record
Abstract
An international agenda has evolved over the past decade to establish hard and soft rules to govern the impacts of the extractive industries. The international community and some resource-rich states have increasingly embraced norms such as transparency in resource governance. This paper explores how multi-stakeholder initiatives such as the Extractive Industry Transparency Initiative (EITI) and the Publish What You Pay (PWYP) campaign have sought to institutionalize transparency in resource governance. By exploring how, why, and to what effect transparency in resource governance has taken hold in a new petro-economy such as Ghana, I highlight two key findings: the interaction between voluntary and mandatory governance mechanisms and rescaling of authority, and the multi-scalar dimensions of resource governance and subsequent lack of focus on sub-national issues. In concluding, I question the transformative potential of transparency in resource governance, which has significant global implications as the demand for energy and non-energy minerals continues to rise.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".