Blossoming Through Creativity: Unveiling Sense of Belonging Among Ukrainian and Syrian Refugee Women in Ontario
Bibliographic record
Abstract
The concept of “belonging” has gained significant attention in policy debates concerning identity, migration, integration, and social cohesion, as it is closely tied to refugee wellbeing and the ability to feel comfortable and at ease in a specific location. This study investigates how Ukrainian and Syrian refugee women in Canada perceive their process of developing a sense of belonging by considering their everyday living practices. The research employs a qualitative approach with an intersectional lens, relying on in-depth interviews conducted with nine Syrian and six Ukrainian refugee women. The notion of belonging is explored from both an individual and collective perspective. Three themes emerged: (1) surviving and thriving; (2) negotiating belonging and identity in a cross-cultural context; and (3) blooming with possibilities in a new home. The study suggests that the transformational impact of migration and displacement directly influences refugee women’s resilience, the development of a sense of belonging, and their everyday practices. Ultimately, the attainment of a comprehensive sense of belonging is multifaceted. It is contingent upon structural factors like stable income, financial security, and secure living conditions, but women’s agency in developing resources, fostering connection, creating beauty, among other factors, is crucially important to belonging.
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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 | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".