Global development financial institutions: the experience of project financing
Bibliographic record
Abstract
There is a significant research interest in project financing as a tool for active development of investment activity in Russia. The novelty of such research lies in studying the role, practical significance, and potential opportunities of development financial institutions (DFIs) in project financing. Using the methods of analysis and synthesis, grouping and comparison, the authors have analyzed the global practice of DFIs, which identifies the main vectors for the institutions’ current activities, achievements, and problematic aspects in promoting project financing. The cases of DFIs in Canada, Germany, and Brazil show the specifics of DFIs’ activities in project financing to solve social and economic problems, to overcome global problems and challenges, and to achieve sustainable development goals. Majority of the research focuses on the organizational, functional, regulatory, and legal support for the activities of DFIs as a tool for implementing investment projects. The other countries’ experience serves as a possible model for application in Russia. The comparison of the main parameters of DFIs in different countries identifies the strengths and weaknesses in their functioning, which is important for the Russian researchers and investors.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".