Resettlement challenges among African refugee women living in Winnipeg
Bibliographic record
Abstract
Abstract This thesis is a qualitative research on resettlement challenges among refugee women from African countries that have experienced war and civil strife. Recent studies have shown that the experience of being a newcomer in a foreign country tests the individual's resilience and coping resources to the limit. Using Urie Bronfenbrenner’s ecological perspectives and the Person-in-Environment theories, this research explores resettlement challenges that refugee women from Africa experience in new ecological contexts. Ecological theories stipulate that individuals are engaged in an on-going transaction with their environments; mutually influencing and being influenced, shaping and being shaped by the world around them (Probst, 2012). Using in-depth, semi-structured interviews, five participants were interviewed. The challenges they identified included struggles with language barriers, housing, finances, employment barriers, learning new parenting skills, and dealing with physical disabilities. Recommendations to alleviate the challenges included streamlined, well-co-ordinated services that are accessible and culturally responsive, as well as practices that are based on recognizing the refugee women’s strengths and involving them in decision-making processes. Key words: African refugee women in Winnipeg, Resettlement challenges, War, Civil Strife
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".