Impact of COVID-19 on the development of infrastructure in Latin America and the Caribbean and the role of Public-Private Partnerships in times of crisis in the regi
Bibliographic record
Abstract
With the support of Governments and Research Institutions in Latin America and the Caribbean, in 2020, the IDB launched the Network of Analysis and Best Practices in Public-Private Partnerships (PPP Network), aiming to drive infrastructure development in the region in terms of quality, sustainability, competitiveness, and efficiency. The PPP Network was created to A) relate public knowledge demands with developed applied research (in other words, research that respond to what policy and project developers want to know about what does or does not work under a PPP framework); B) To systematize information: arrange structured and organized information for projects analysis, from investment decisions to financing ones; C) To generate and coordinate current evidence: through the development of analytical works using public information available through the network, as well as to relate regional applied research to enhance knowledge creation. Beginning in 2020, and by a Public Call to governments in the region, a series of topics were determined which gathered common interests in the development of infrastructure under PPP schemes in five main areas: Regulation and Institutions; Feasibility and Structuring sustainable projects; Financing of projects; Risk management and monitoring; and Evaluation, Performance, and Impact. The document “Impact of COVID-19 on the development of infrastructure in Latin America and the Caribbean and the role of Public-Private Partnerships in times of crisis in the region” responded to such demand and was elected under an exceptional category over the pandemic circumstances, through a competitive call for proposals.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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".