Why is the resettlement in a third-country the chosen solution by the Bhutanese refugees? A personal answer to a political problem
Bibliographic record
Abstract
This paper gives voice to the motivations that drove thousands of Ethnic Nepalis from Bhutan to choose resettlement as a solution to their neverending plight. Expelled by their own country where they started to be perceived as a source of instability in the 1980s, the Bhutanese refugees have found refuge in Nepal for the last 20 years, surviving an inhospitable policy reluctant to integrate them. From 2007, the governments of Denmark, Australia, Canada, the Netherlands, New Zealand, Norway and the United States started to host the Bhutanese refugees willing to be resettled in a third-country. Since 300 Bhutanese refugees are currently living in Denmark, we decided to interview the 41 resettled in Haslev, a town in Faxe municipality, about 60 km from Copenhagen. Although refugees are generally perceived as a passive object of humanitarian assistance, our findings show people that they are aware of the difficulty to be repatriated, willing to end a life of idleness in the camps, hoping for a better future and craving for citizenship, and hence the refugees have actively chosen to start over in a foreign country despite the hardships that they had to encounter. The resettlement in fact proves to be a long process that does not end with the arrival of refugees, but it actually continues in the host state.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".