The Impact of Viet Nam’s Socialization Policy on Education and Health Equity Among Disadvantaged Children
Bibliographic record
Abstract
The Xa hoi hoa (socialization) policy was officially adopted by the government of Viet Nam in the late 1990s as a coping strategy for its acute funding shortage in public services with the intent of improving social equity. There was hardly any assessment on the policy to learn its impact on social equity as documented in its key document. Using secondary data from UNICEF’s Multiple Indicator Cluster Surveys conducted in Viet Nam and Viet Nam Household Living Standards Surveys, this study examined the policy’s impact on education and health outcomes among disadvantaged children’s cohorts: the rural, the ethnic minorities and the poor, before and after two milestones of policy transformation: 2006-2008 and 2014-2015. The theory of social equity in public administration and a theory of change were adopted to design a research framework on social equity. One-sample chi-square tests and logistic regression tests were used to examine changes before and after the two milestones. Findings of this study indicated the socialization policy yielded considerably good impacts on rural children in terms of school attendance, school completion, access to healthcare, and health insurance. It was not the case for the poor and ethnic minorities regarding school attendance and access to healthcare. The findings suggested that more policy interventions need to be undertaken, such as effective monitoring and control measures to ensure proper policy implementation, no abuse of policy’s incentives on taxation and land lease, and effective social protection for vulnerable groups. Disadvantaged children’s cohorts would then enjoy health and education equity as the policy intent, contributing to sustainable development and positive social change.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".