Assessment of the Impact of the Crisis on New PPI Projects : Update Five
Bibliographic record
Abstract
Investment commitments to infrastructure \n projects with private participation (Private Participation \n in Infrastructure (PPI) projects) reaching closure in \n developing countries grew by 22 percent in the third quarter \n of 2009, and by 10 percent in the first three quarters of \n the year, compared with the same periods of 2008. These \n growth rates indicate a strong recovery from the 54 percent \n decline in the second half of 2008 compared with the same \n period of 2007. But investment grew selectively, \n concentrated in large energy projects in a few countries: \n Brazil, India, and Turkey. The Russian Federation, by \n contrast, saw a sharp decline in investment as a result of \n the global financial crisis and the end of the RAO UES \n privatization program. If these four countries were \n excluded, investment in developing countries would have \n fallen by 49 percent in the third quarter of 2009, and by 5 \n percent in the first three quarters, compared with the same \n periods of 2008. Among sectors, energy was the only one with \n investment growth in 2009, thanks to activity in greenfield \n power plants. Across sectors, large projects (US$500 million \n or more) accounted for the investment growth. Private \n activity as measured by number of projects remained slower \n than before the full onset of the financial crisis. The \n number of projects reaching closure was 27 percent lower in \n the third quarter of 2009, and 10 percent lower in the first \n three quarters, than in the same periods of 2008. These \n trends suggest greater project selectivity. Indeed, the \n large projects that are reaching closure are characterized \n by strong economic and financial fundamentals and the \n backing of financially solid sponsors and governments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".