Ad-hoc Expert Group Meeting on Mining Cluster Development in Africa: Welcome address By Mr. Josue Dione, Director Sustainable Development Division
Bibliographic record
Abstract
Welcome address By Mr. Josue Dione, Director Sustainable Development Division at the Ad-hoc Expert Group Meeting on Mining Cluster Development in Africa. Mr. Dione, on his remarks highlighted that, ECA believes that the poor performance of African countries in this area is not unavoidable and that, as in Canada and Australia, minerals represent potential wealth. Central to achieving these objectives is the existence of capable institutions, sound macro-economic underpinnings, and good governance. Understanding in detail the reasons for the poor performance of the mineral sector in Africa is at the core of the study. More important, however, is that in undertaking this study, we hope to advance and contribute knowledge on the fundamentals of mineral cluster development and identify opportunities for and constraints to such development.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".