Toward a Language Education Policy for Immigrants in Thailand: Lessons Learnt from Europe and Canada with a Case Study of Phuket Island
Bibliographic record
Abstract
Phuket, the most famous tourist island in Thailand, receives a large number of immigrants, especially from Myanmar, into its workforce. As a result, related immigration concerns are often linked to education. As such, a high number of children of these immigrants are not directly accepted by and assisted with suitable arrangements into local schools despite a linguistic priority which has been accepted internationally as a human right that children should learn in their mother tongue for improved educational success. In this study, the issue is approached by presenting a brief review of mother tongue instruction in Europe and Canada with the aim to posit for Thai policy makers to consider initializing a suitable educational language policy specifically for the children of immigrants in Thailand by employing Phuket Island as a case study. The advantages and drawbacks from western countries could provide lessons for Thailand in coping with the issue. It is proposed that enabling languages in education for immigrant children should be set out with a clear vision and strategy, and that also some educational authority should be decentralized to local governments who can respond effectively to the needs of stakeholders in the areas. Moreover, budgetary and management plans are crucial for successful implementation. Lastly, appropriate international collaboration will drive the policy toward success. Hence, linguistic phenomena within immigration and minorities in Thailand, as well as in other ASEAN countries, could be approached by moving away from the historically European standard language center, as noted by Halliday (2007).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".