Insight Grant Competition: Does Transparency Lead to Accountability? A Two-Country Study of Local Implementation of the Extractive Industries Transparency Initiative in West Africa
Bibliographic record
Abstract
A major challenge confronting many resource-rich developing countries is severe and systemic corruption, which prevents them from getting the most value from their oil, gas and mining sectors. The Extractive Industries Transparency Initiative (EITI), a global initiative launched in 2002 by the UK government to promote better management of resource revenues, is strongly supported by the Canadian government, one of 15 donor partners. The EITI expects participating governments to disclose the royalties and taxes they receive from the extractive sector, and oil and mining companies to report what they pay to government. While increased transparency about the revenues received from extraction is expected to produce more accountable national and local governance, the EITI Secretariat acknowledged that this outcome is not always achieved and has called for greater effort to be directed to ensuring accountability at the local level. This research responds to this call by focusing on EITI implementation at the local level in Nigeria and Ghana, two important EITI-compliant countries.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".