Exploring Social Bridging, Sense of Belonging, and Integration Amongst the Syrian Refugee Community
Bibliographic record
Abstract
The civil war in Syria caused an upheaval to all aspects of life for its citizens, resulting in an unprecedented number of Syrians arriving in Canada as refugees. While government and settlement agencies responded by addressing their immediate needs, other aspects of their integration, specifically their social integration, were much less prioritized and minimally resourced. This study drew on Ager & Strang’s (2008) Domains of Integration framework and their description of social bridging to explore this aspect of social integration of refugees in greater detail. A qualitative descriptive methodology was applied to explore how Syrian refugees describe their experiences of building social bridges in Canada, and how these bridges impact their sense of belonging and overall integration. Semi-structured interviews were conducted with twelve adult members of the Syrian refugee community, and thematic analysis was used to interpret the data. This study found that: social bridging is influenced by the conditions that shape if social bridges are formed; friendliness, intentional connections, and neighbourly relations are valued social bridges; and social bridging promotes adaptation and sense of belonging outcomes for refugees. The insights that emerged from this study contribute to a better understanding of the interrelationship of these concepts for Syrian refugees, and establishes an foundation to explore social bridging in greater depth for enhancing theory, as well as to improve social bridging support for refugees in practice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.015 |
| 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".