Analysis of Foreign Direct Investment Attraction in Vietnam Real Estate in the Pandemic Condition
Bibliographic record
Abstract
The economy of Vietnam is a socialist-oriented emerging market that heavily depends on agriculture, tourism, raw material exports, and foreign direct investment. Vietnam is industrializing and modernizing from a low starting point with weak and small economic resources. This is one of the huge obstacles to the development of Vietnam's economy. Therefore, mobilizing and leveraging foreign direct investment are very important. In the COVID-19 pandemic, many large international corporations and enterprises are looking for investment opportunities to diversify their supply chains and limit their overdependence on the Chinese market. Vietnam has emerged as one of the brightest candidates for this capital flow due to its success in epidemic prevention. While foreign direct investment has been declining globally, foreign direct investment in Vietnam has rebounded in the second quarter of 2020 and has been on an upward trend. The real estate market remained an active segment in the third quarter of 2020. This article uses comparison, logical and statistical analysis methods to study foreign direct investment in real estate in Vietnam. The contribution analyzes the factors that influenced the desire of foreign investors to invest in the Vietnamese real estate market, based on publicly available data sources of the Foreign Investment Agency, Vietnam Ministry of Planning and Investment; and the General Statistics Office of Vietnam. Solutions at the state level to stimulate investment in Vietnam are proposed.
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) | 0.004 | 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".