Assessing developing countries students' achievements in international educational testing by socio-economic status across regions, areas, and gender: a case of Vietnam Participating in PISA 2012 and 2015
Bibliographic record
Abstract
The literature shows the absence of international educational testing regimes of low-income developing countries. This paper addressed three neglected issues related to Vietnamese students' achievements: 1) the link between family background measured by socio-economic status (SES) and educational skills measured by PISA test scores; 2) the association between low and high-parental SES and students' skills; 3) the link between proficiency levels and SES gradient - the issue more important to the success of young adults. Findings presents distributions of SES gradient in academic skills across Vietnam, regions and gender in 2012 using a comparable measure between parental SES and the 2015 reiteration of test scores. A cross-areas variation identifies indirectly the differences in regional school resources that may lead to inequalities of opportunity. The SES gradient estimations not only relate to math, reading and science skills, but also to proficiency levels in the same cognitive domains at different years.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".