Will solar energy escape the natural “resource curse”?
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
The resource curse haunts countries whose economies have become dangerously specialized in the exploitation of a single resource. This curse threatens countries whose economies are poorly diversified and oriented mainly towards the export of their non-renewable natural resources, such as oil. What about the exploitation of an abundant renewable natural resource such as solar energy? Based on a case study of six solar power plants in six African countries (Burkina Faso, Madagascar, Morocco, Rwanda, Senegal, and South Africa), this paper analyzes the extent to which the impacts of the exploitation of these energy systems contribute to this curse. The research method is qualitative (296 interviews) and quantitative (use of a sustainability index), making it possible to analyze the impacts of solar power plants on four levels (local, regional, national, and international). Our results reveal four findings symptomatic of the resource curse: (i) the emergence of conflict situations, (ii) fragile local development, (iii) latent financial risk, and (iv) limited economic development leverage. In short, the resource curse linked to the use of renewable energies seems to bring another challenge to the landscape of energy transition.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 it