Refugee Perceptions of Post-Migratory Stressors Impact on Pre-Existing Trauma
Bibliographic record
Abstract
Refugees are an ever-growing population across the world. Refugees leave their country to survive from the horrors and traumas many experiences. They arrive in countries oftentimes with little to no language proficiency, housing, finances, or employment. The stressors many of them face mixed with the trauma they experienced pre-arrival can be a heavy burden to carry. The purpose of this case study is to understand the perceptions of post-migratory stressors impact on pre-existing trauma for refugees at Matthew House in Vancouver, Canada. A constructivist approach is taken since the focus is on the situation from the participants’ point of views and looks to advance areas of furtherance in the area of social justice for minority populations. The goal is to add to the discussion on how to better assist refugees when they arrive to countries such as Canada so they can find healing and adjust quickly to be an asset to the community.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".