A <scp>S</scp>wiss‐Army Knife? A Critical Assessment of the Extractive Industries Transparency Initiative (EITI) in <scp>G</scp>hana
Bibliographic record
Abstract
Abstract Within the current global atmosphere where a universally accepted police force is nonexistent, there are several voluntary norms and codes of conduct that exist to guide how corporations behave worldwide. These have come as a result of many years of poor performance in the areas of social, financial, and environmental responsibility. Such norms are expected to prescribe and proscribe certain types of corporate behavior but when one examines the reality on the ground, the story is not that straightforward. This article assesses the effectiveness of the Extractive Industries Transparency Initiative (EITI) in the Ghanaian context with a focus on the mining sector. Based on primary qualitative data the argument is that even though the EITI is performing some function, it has ways to go before it can become an across‐the‐board viable tool for transparency and proper accountability. Five prevailing weaknesses are discussed to underscore this case.
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.
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 | 0.078 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".