Resilience and Canadian SOGIE Refugees: An Application of the "Ordinary Magic" Model
Bibliographic record
Abstract
SOGIE (sexual orientation and gender identity expression) refugees flee their home countries due to stigma and persecution, often undertaking dangerous journeys to seek safety in more inclusive nations like Canada. Although Canada has welcomed many SOGIE refugees, there has been limited research on the resilience they demonstrate throughout their integration process. Guided by Masten’s “ordinary magic” resilience theory, this study examines the immediate challenges faced by SOGIE refugees and the factors that support or hinder their resilience. To analyze interviews with 32 SOGIE refugee participants, I used reflexive thematic analysis (RTA) supported by NVivo 12 software. Four key themes were identified: receiving vital support during critical times, the ability to live authentically in Canada, expressing deep gratitude for Canada’s support, and a strong desire to contribute to Canadian society. All participants reported that their journey would not have been possible without the generous support of loved ones, community organizations, and the Canadian government. These findings support the “ordinary magic” theory of resilience, which emphasizes the importance of everyday social supports over rare internal traits in fostering resilience. In addition, I identified two major barriers to resilience: a reluctance to engage with members of their own diaspora due to past trauma, and an intense fear of unintentionally committing a crime and facing deportation. Regarding the latter, participants noted that Canadian laws are often not well-known or well understood. These insights offer valuable contributions to our understanding of the Canadian SOGIE refugee experience.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".