Trends in the African Construction/Plant Building Market and Implications for Korea
Bibliographic record
Abstract
Africa is the poorest continent in the world in terms of public infrastructure. In any country with functioning public infrastructure, and roads form the backbone of transportation; responsible for the 80-90% of movements of people and goods. In Africa, however, only 20% or so of existing roads have been paved. The vast majority of existing railways was laid during the colonial era and is now obsolete, unable to function properly. Much of its port and airport facilities are similarly outdated, becoming, in effect, the major obstacle to the continent's economic development. Particularly conspicuous as well is the absence of proper electricity infrastructure. Almost 800 million Africans live in the sub-Saharan region, but the aggregate power generation capacity of the region lags behind the capacity of Spain (with a population of 45 million). If South Africa is not counted in with sub-Saharan Africa, the region's power capacity is lowered to the level of Argentina. Nearly a quarter of the existing power facilities are out of order and obsolete. Thirty or so African countries, therefore, experience power outages on a daily basis with serious economic losses as a consequence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".