The Bhutanese (Lhotsampa) refugees of Winnipeg: A journey of empowerment, self-efficacy, and resiliency
Bibliographic record
Abstract
In 2007, approximately 108,000 Lhotsampa refugees had been displaced from their native Bhutan and were living in refugee camps in the neighbouring nation of Nepal. With the assistance of the Nepalese government, the Core Group on Bhutanese Refugees, and the United Nations High Commissioner for Refugees, a resettlement initiative resulted in the redistribution of Lhotsampa refugees to several developed nations throughout the world including Canada. This study explored the stories of a group of resilient Bhutanese (Lhotsampa) refugees – from their expulsion from Bhutan to their lives in refugee camps in Nepal, and to their final journey to Winnipeg, Canada, in search of a more harmonious and peaceful life. The theory section of this study examines several themes including the effects of traumatization, the social process of empowerment, the theoretical perspectives of self-efficacy and resiliency, and the phenomena of culture. To supplement this research query two methodologies were deployed – narration and asset mapping. Narration presents a holistic picture of the events and experiences of the Lhotsampa refugees and their migration to Winnipeg. Asset mapping, on the other hand, outlines the tangible and intangible assets the Lhotsampa refugees identify and utilize to support their transition from life in a refugee camp to life in Canada.
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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 | 0.022 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".