International Experience and Lesson for Vietnam: "Raising Capital for Investment in Infrasreuctural Development in International Intergration"
Bibliographic record
Abstract
It is essential for each nation to invest, improve and retrofit its infrastructure. However, with the diverse participation of all economic sectors, each country has its own method of calling and attracting funds for infrastructure development.Vietnam is in a strong urbanization context, so the development of infrastructure and capital investment in infrastructure development is a very important issue. According to the current development trend, the demand for investment capital for infrastructure has exceeded the state budget's ability. At the same time, traditional solutions in order to increase public investment resources are now hampered by the fact that the Vietnamese budget can not expand for infrastructure investments. Therefore, the infrastructure investments in the coming period must be based mainly on the experience of developed countries.Studying the practical experience of mobilizing capital to develop infrastructure in ThaiLand, Malaysia, Singapore, Chile, China and India shows that developed countries have effective solutions to mobilize resources to invest to infrastructure development, creating a development motivation for socio-economic. This article explores the experience of raising capital for investment in infrastructure development in some countries who have many similarities in socio-economic situation with Vietnam, thereby we can learn a lot of good experience for Vietnam
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".