“I came by the bicycle so we can avoid the police”: factors shaping reproductive health decision-making on the Thailand-Burma border
Bibliographic record
Abstract
For over half a century, political conflict combined with an overall lack of economic develop-ment has resulted in the displacement of millions of people both within Eastern Burma and to neighbour-ing Thailand. Given the overarching context, in conflict-affected regions of Burma, women face tremend-ous challenges in trying to obtain high quality, comprehensive reproductive health services. Drawing from interviews we conducted in Tak province, Thailand with 31 migrant and refugee women from Burma, this article explores women’s lived experiences along the border and focuses on the ways that complex, overlapping barriers impact women’s reproductive health decision-making at different points in their reproductive lives. Our results show that reproductive experiences are highly dependent on the woman’s place of living mixed with her legal status and financial resources. Combined with socio-cultural taboos and externalized and internalized stigma, these dynamics blend to place constraints on women’s autonomy and self-actualization. The way in which women’s experiences are shaped by these barriers offers insights into priorities for education and programming to help improve reproductive health services in this protracted conflict setting.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".