Global Energy Governance and Natural Resource Transparency in Africa: Assessing the Extractive Industries Transparency Initiative (EITI) in Nigeria and Ghana
Bibliographic record
Abstract
The EITI has been held up as an initiative capable of strengthening governance by improving both government and business transparency in natural resource extraction. At the heart of this desire for transparency is the believe that better management of natural resource revenues will enhance trust, address endemic poverty and corruption, and prevent resource related conflict and instability. This paper examines the EITI regimes in two resource-rich African countries - Nigeria and Ghana. While there are case studies of EITI, the existing literature lacks independent cross-national research. This study fills this research gap by comparing the Nigeria Extractive Industries Transparency Initiative (NEITI) and the Ghana Extractive Industries Transparency Initiative (GEITI). Giving the multi-stakeholder engagement approach of EITI, data for the study will be elicited from documentary search and interviews with key stakeholders in the implementation of EITI in these countries government, oil/gas and mining companies, and civil society groups. Through such comparative study, the study aims to assess the impact/challenges of EITI, and identify important lessons that can be learnt from the over 10 years of implementation, particularly regarding strategies of stakeholder engagement, key drivers of the reforms, and the salient factors or conditions that account for success or failure. /
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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 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".