Impact of land-acquisition induced resettlement policy on the ethnic household income in mountainous Vietnam
Bibliographic record
Abstract
Land acquisition and resettlement issues related to hydropower and irrigation works have always been one of the hot issues in Vietnam mountainous areas for many years. Although the Government has introduced many policies to ensure the rights of resettled people, as well as protect their lives, the effectiveness of these policies seems to be still insignificant, because many resettled people still face many difficulties in their daily life, especially income. The research is conducted within the project titled “The urgent issues in resettlement implementation for the ethnic minorities in Vietnam mountainous areas” and funded by National Council for Science and Technology Policy in 2016-2020 (CTDT/16-20) under Committee for Ethnic Minority Affairs. Applying Likert-scale and Propensity Score Matching (PSM), this study shows that 34% of the resettled households have a lower income, specifically estimated to be 8.0 - 13.1 million VND/household/year or 1.7 - 3.0 million VND/person/year lower than the income of the controlled group. However, agricultural income is not significantly different between resettled households and controlled households. This article only focuses on clarifying the impact of the resettlement policy on the general income and agricultural income of ethnic minority households; while methods to create jobs, increase income, and reduce poverty sustainably for ethnic minority households in the resettlement sites should be conducted in another research in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".