Investment and governance of Africa’s resources: economic diversification for development
Bibliographic record
Abstract
It is with great pleasure that we present Volume 8 Issue 1 of Nigeria's Journal of Sustainable Development Law and Policy -a Special Issue on Investment and Governance of Africa's Resources: Economic Diversification for Development.This Special Issue brings together contributions from leading academics and practitioners who are engaged in implementing initiatives relating to investment and governance of the extractive industry in Africa and the Middle East.The start of the new millennium has witnessed a new scramble for Africa's resources.In the span of a decade, both the volume and value of minerals, metals and oil have grown dramatically.In some cases, the costs of certain metals (such as copper) have more than tripled in value, with the average cost for most minerals and metals at least doubling.The market has also witnessed an almost exponential rise in global oil prices.Similarly, despite almost decades of sustained economic growth, with many countries achieving 5 per cent (or more) GDP growth per annum over the last decade, this has not translated into a significant increase in jobs, with unemployment rates within the largest population group -the youth -at the same levels today as they were in 2000.While all of this has generated massive profits for host governments and the international extractive industry, the developmental needs of many African countries, in which many of these natural resources are to be found, remain as stark as ever.Proportionately, Africa has the highest number of people living in extreme poverty.In all the areas examined and measured by the Millennium Development Goals, although significant improvements in some areas have been made, Africa remains far behind the rest of the developing world in its progress towards therealization of these targets.Despite extensive reserves of oil and natural gas, an abundant wealth of minerals and metals, many on the continent continue to suffer extreme levels of poverty, inadequate health and education services, and poor infrastructure.In sum, the global commodity
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".