North and Sub-Saharan Africa Energy Security Policies in the 21st Century
Bibliographic record
Abstract
Renewable energy sources are of vital importance for solving the problem of energy stability, especially solar energy, which should become the cheapest source of electricity in almost all African countries by 2030. International financial institutions should step up the mobilization of private capital, act as first movers to absorb risk and protect investments. This would help promote projects that support vulnerable populations, lay the foundations for sustainable economic growth and ensure that Africa becomes an attractive destination for energy investment. Many people associate technologies like solar and wind power with the efforts they make to combat climate change. But for the world's most vulnerable populations, they are much more than a clean energy solution. In terms of energy sources, the continent of Africa has a huge potential that has remained largely untapped. Despite having large expanses for 60% of the best solar resources globally, Africa has about the same installed solar PV capacity as Belgium, a small country not known for its amount of solar energy utilization. There is also great potential for hydro, wind and geothermal energy in many African countries, and these energy sources can play an important role in diversifying and securing electricity supply. Responsible use of the continent's natural resources will be essential to its development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".