Natural Resource–Based Development in Africa: Panacea or Pandora’s Box?
Bibliographic record
Abstract
There is no question that Africa is endowed with abundant natural resources of different magnitudes. However, more than a decade of high commodity prices and new hydrocarbon discoveries across the continent has led countless international organizations, donor agencies, and non-governmental organizations to devote considerable attention to the potential of natural resource–based development. Natural Resource–Based Development in Africa places a particular emphasis on the actors that help us understand the extent to which resources could be transformed into broader developmental outcomes. Based on a wide variety of primary sources and fieldwork, including in-person interviews and participant observations, this collection contributes to both scholarly and policy discussions around the governance and economic development roles of local entrepreneurs, transnational firms, civil society groups, local communities, and government agencies in Africa’s natural resource sectors. Natural Resource–Based Development in Africa explores the impact that these actors have on regional trends such as resource nationalism and local procurement policies as well as grassroots-related issues such as poverty, livelihoods, gender equity, development, and human security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".