South Africa infrastructure 'boom' may underwhelm
Bibliographic record
Abstract
Subject Infrastructure outlook for South Africa. Significance Earlier this month PricewaterhouseCoopers estimated that South Africa will account for a third of all sub-Saharan Africa's estimated 180 billion dollars in infrastructure investment to 2025. Yet even this is likely to be insufficient to address years of neglect, particularly of urban and basic service infrastructure. Preference for 'mega', state-funded projects could deflect attention from more prosaic but essential needs. Impacts Concern over poor service delivery could cost the ruling ANC key cities such as Port Elizabeth in the 2016 local government polls. Fiscal strains will drive a clampdown on anti-competitive behaviour in the building industry, which has previously heightened costs. Large cities are likely to turn to bond markets to raise funding for infrastructure expansion and upgrades necessitated by urbanisation. Power outages will dampen already low growth -- 1.3% in the first quarter 2015 -- especially in job-rich sectors such as manufacturing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.423 | 0.134 |
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".