To build a home: the material cultural practices of <scp>K</scp>aren refugees across borders
Bibliographic record
Abstract
Civil strife between the central military government and dissident groups in Burma has caused the displacement of Karen refugees since the late 1950s. Fleeing the practices of armed groups that include the confiscation of farmland, forced labour and gendered violence, Karen refugees seek refuge along the Thai–Burma border where they are not recognised as ‘legal persons’ and therefore forced to remain in camps established from the late 1980s. This long‐standing conflict has resulted in one of the most protracted refugee situations in the world. Since 2006, the Canadian government has been re‐settling Karen refugees from this border region. Situated within this context, in this paper I explore Karen refugees' creation of ‘home’ through an examination of their material cultures. I argue that while the notion of home becomes destabilised at different junctures of displacement, phases of mobility and immobility nurture the creation of home. Through their material engagements with dwellings at the Thai–Burma border and in Canada, Karen refugees' everyday practices reinforce the specialised role of place within flows of movement. The bordering projects at the Thai–Burma boundary are reinforced and troubled by both the presence of these shelters, which serve as physical reminders of the ongoing conflict, and the mobility and immobility of bodies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".