Support Networks of Immigrants in Canada: A Multilevel Multinomial Analysis of Social Support
Bibliographic record
Abstract
This study examines the role of support networks in the integration process of Ghanaian immigrants to Canada. Although social support networks have been widely argued to shape the immigrants’ pre- and postmigration experience, their presence and roles are neither self-evident nor constant. Common conceptualizations of social support exchange often perceive support as unidirectional, flowing only from network members to immigrants. This underlies a rather linear notion of support networks among immigrants. Given this, this study investigates the directionality of support regarding emotional, instrumental, and informational support and the extent to which closeness, delineated as familial (kinship), perceived (importance of relationship), temporal (frequency of interaction), and geographic (location) influence these support exchanges. The study employs a multilevel multinomial analysis of 172 egocentric networks in Toronto, Canada, using a social network analysis approach. Results show that geographic closeness was consistently less important than kinship, temporal, and perceived closeness in both emotional, instrumental, and informational support—regardless of directionality. This broadly support the assertions about “death of distance” and that spatiality might no longer be a key influencer within the social support networks of immigrants.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".