Migrant Learning Centers on the Thai-Myanmar Borderland: Giving New Meaning to “Live and Learn”
Bibliographic record
Abstract
“It is the weakest among us who need education the most and we cannot stand by as they are being excluded,” said Kishore Singh, UN Special Rapporteur on education, following the adoption of the Incheon Declaration at the World Education Forum in May 2015. He also called for “strategies to address inequality by focusing on girls and women, ethnic minorities, persons with disabilities and children living in conflict-affected areas, rural areas and urban slums.” One especially vulnerable group that has been denied their right to education are the children caught up in forced migration along the Thai-Myanmar border. One strategy, highlighted in this article, for ensuring these children have access to quality early childhood education and complete primary education with effective learning outcomes, is nonformal, migrant alternative learning centers. The authors present a case study of one such center, New Blood School and Boarding House, to demonstrate the critical role alternative care systems can play in meeting the needs of marginalized children and ensuring that no child is left behind.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".