Drawing-voice as a methodological tool for understanding teachers' concerns in a pilot Hmong–Vietnamese bilingual education programme in Vietnam
Bibliographic record
Abstract
This paper illustrates how a methodological tool called ‘drawing-voice’ can be used to demonstrate qualitatively what statistical and policy data are not able to reveal regarding the educational realities of Hmong minority communities in northern Vietnam, particularly with regard to the role of local language and culture in school. This paper describes the approach of using drawing to stimulate authentic discussion, which is then analysed in light of the current conditions of educational services for Hmong speakers. This visual methodology was seen by the participants themselves as culturally appropriate. The drawing-voice activities conducted with teachers from Hmong community schools in northern Vietnam have demonstrated that teachers' identities and practices are influenced by certain linguistic, cultural, and environmental issues. According to the drawing-voice participants, the reasons for educational inequity include the use of Vietnamese as the language of instruction, a lack of cultural sensitivity in the curriculum and by some teachers, a lack of school materials, and difficult physical conditions such as geographic isolation, poor road conditions, and deterioration of schools. This combination of conditions explains why so few ethnic minority learners survive the school system long enough to become professionals and how the lack of Hmong teachers contributes to this vicious cycle.
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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 | 0.042 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".