State Management in Building a New Rural Area in Vietnam: A Research in Muong Tra District, Dien Bien Province
Bibliographic record
Abstract
Vietnam's rural area accounts for 65% of the population, providing over 70% of the labor force for the national economic sectors, of which 51% of the workforce is working in the agricultural sector. This is a sector that creates many jobs for rural areas, contributing 30% of GDP to the national economy. In the current period and the coming years, Vietnam's rural agriculture still plays an important role in the country's socio-economic development. However, Vietnam's rural areas are facing new difficulties and challenges, the growth of agriculture is slow and unsustainable; Farmers' living standards are low and slowly being improved; The disparity in living standards between urban and rural areas, and between regions and regions is tending to expand, especially in mountainous rural areas with unfavorable natural conditions, with many ethnic minorities living together, the gap between the rich and the poor is increasing. That creates conflicts and instability in society, threatening the sustainable development of the country. This article focuses on analyzing the state management situation on new rural construction in Muong Cha district, Dien Bien province, Vietnam, pointing out the achievements, limitations, causes and some recommendations for improvement. State management on the construction of a new countryside in Muong Cha district, Dien Bien province, Vietnam in the context of world economic integration.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".