Refugees’ resettlement in a Canadian mid-sized Prairie city: examining experiences of multiple forced migrations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to discuss the experiences of multiple forced migrations and resettlement among two refugee families in a mid-sized Canadian city. Design/methodology/approach – Case studies are located within the contingencies of the participants’ lives and the meanings they provide to the events. A postcolonial feminist perspective guided the data analysis to explore the micro-level of individual experiences that unfold within a raced, gendered, and classed reality. Open-ended interviews, participant observation, and field notes were used to collect participants’ perspectives. Data were collected until saturation occurred. Findings – An in-depth analysis of these two case studies revealed that lack of choice and lack of access to health and social services affect health through constant revival of traumatic past experiences prior to arrival to Canada. Three themes emerged from the data analysis: first, shared experiences of forced migrations; second, the past and present: construction of new identities; and third, resettlement challenges and opportunities. These themes overlap and intersect to shape the experiences of double forced migration. Research limitations/implications – This research has limitations related to the sample size but provides data on a topic that deserves more attention in the field of immigrant and health studies. The authors argue that health and social professionals must resist “finalizing” refugees into disempowered identities that undermine human agency. Originality/value – Research on resettlement experiences after forced migration is a burgeoning field in refugee studies. The originality lies in drawing on Bahktin to develop practical implications to guide health and social practice in this area marked by racialization and fundamentalism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".