Private activity in transport slows down in 2009, but remains concentrated in road projects
Bibliographic record
Abstract
Private activity in transport declined for the third consecutive year in developing countries. Investments fell by 20 percent and the number of projects dropped by 19 percent in 2009 compared with 2008, according to recently released data from the Private Participation in Infrastructure Database. New private activity in transport was concentrated in road projects, and in a few large developing economies such as Brazil, India, and Mexico. In 2009, 50 transport projects with private participation reached financial or contractual closure in 20 low- and middle-income countries. These projects involved investment commitments of US$19.2 billion. Transport projects implemented in previous years received additional commitments of US$2.5 billion, bringing total investment in 2009 to US$21.7 billion. The private activity was concentrated in the first two quarters of 2009, which accounted for 75 percent of investment in new projects and 64 percent of new projects. Similar concentration occurred in 2008 before the full onset of the global financial crisis. The backlog of projects from the second half of 2008 and the easing of financial constraints in the first half of 2009 (compared with the second half of 2008) may partially explain the concentration of PPI activity in the first half of 2009. Preliminary data suggests that activity by investment and number of projects in the first quarter of 2010 was similar to that reported in the first quarter of 2009.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".