The Role of Local Government in Private Investment Intentions: A Case Study in Vietnam
Bibliographic record
Abstract
The private economy is regarded as one of the nation's most important economic components.Local governments take a variety of steps to attract private investment.In this study, the methodology is based on a SEM (structural equation modeling) model utilizing data gathered from 265 private businesses.According to the research findings, all six factors influence the investment intentions of businesses: the infrastructure, labor, administrative procedures, land policy, transparency, support from local government.The method is based on a SEM (structural equation modeling) model with 265 private enterprise data.According to the findings of the study, the following six factors influence the investment intentions of businesses: the infrastructure, labor, administrative procedures, land policy, transparency, support from the local government.Moreover, fsQCA (fuzzy set qualitative comparison analysis) with the same dataset reveals that investment intention is composed of two groups of factors.The infrastructure, labor, support from the local government, administrative procedures, and transparency comprise the first group.The second group includes labor, support from the local government, land policy, administrative procedures, and transparency.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".