The other resource curse: extractives as development panacea
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since 2010, many African governments have challenged twenty years of extractive sector liberalization that has played a key role in unlocking mineral riches and attracting foreign direct investment. The potential for extractives to drive economic structural transformation is intuitively attractive, the Africa Mining Vision (2009) document providing a primary template. Geological inheritance alone, however, is not a panacea for economic development, industrialization or poverty alleviation. While much attention to the ‘resource curse’ has identified the problem of excessive rent-seeking and the consequent impact on elite consolidation, democracy, governance and macroeconomic distortions, a more fundamental problem, the ‘other resource curse’, may be an overlooked driver: a lingering assumption that mineral resources should straightforwardly provide significant revenue streams for public goods, inputs for industrial transformation, and extensive employment. Geology alone is neither conducive nor antithetical to economic development. Stakeholders require a more comprehensive understanding of the possibilities and limits of extractives in contemporary Africa.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it