Bibliographic record
Abstract
Agriculture is an industry with potential and advantages for development, but it is increasingly difficult to attract foreign direct investment (FDI) flows. Up to now, the results of attracting FDI inflows into the agricultural sector have many limitations, not really reaching the industry’s potential. This study will assess the current situation of attracting foreign direct investments into the agricultural sector in Vietnam in terms of FDI capital scale, FDI capital structure based on agriculture standard, investment method, investment partners and by investment recipients. The Red River Delta is one of the two Vietnamese economic regions with highly agricultural production. With the tradition of agricultural production and many favorable natural, economic and social conditions, the Red River Delta can further develop into a major agricultural production area of the country, contributing to economic development of the region and the whole country. However, FDI investment in agriculture in the region is modest compared to the potential of the industry as well as compared to other sectors in the region. While FDI inflow into Vietnam and other sectors in the region tends to increase strongly, FDI into agriculture is very low and has not grown for a long time, which is contrary to the trend of FDI to other sectors of the Red River Delta as well as the whole country and also contrary to the FDI flows to global agriculture.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".