Designed for disruption: Lessons learned from teacher education in Myanmar and its borderlands
Bibliographic record
Abstract
Due to protracted armed conflict, recurrent political crises, widespread structural disruption, and multi-dimensional oppression, teacher education in Myanmar and its borderlands operates within parallel state and nonstate systems. This article draws from a qualitative study that used complexity theory to examine how parallel ethnic and indigenous teacher education systems navigated disruption during the COVID-19 pandemic. The pandemic largely paralysed the provision of teacher education in Myanmar’s central government system. In contrast, the actors interviewed for this study who work in parallel systems pivoted and re-developed their programming to meet the need on the ground. The use of de-centralised approaches and flexible programming, and their ability to adapt the response to emerging needs and to operate with minimal resources, may signal that these parallel teacher education systems are designed for disruption. How such systems have continued to function amid complex emergencies may offer insights for researchers investigating the ways in which teacher education systems work in other crisis contexts.
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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 | 0.001 |
| 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) | 0.007 | 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".