Survival and social support networks: visual narratives of labour market integration among highly skilled African immigrants in Quebec
Bibliographic record
Abstract
Despite the growing presence of highly skilled African immigrants in Quebec, Canada, there is a paucity of research on their adaptation processes, specifically regarding survival and social support. This study addresses this gap by investigating the survival strategies of highly skilled African immigrants (HSAI) in Quebec, utilising photovoice to vividly capture their social support systems and survival cultivation. Drawing on Ungar's (2012. The Social Ecology of Resilience: A Handbook of Theory and Practice. Springer) ecological perspective of survival, and Tardy’s model (1985. “Social Support Measurement.” American Journal of Community Psychology 13 (2): 187–202) of social support, this research elucidates the strategies employed by HSAIs to thrive. Data collection involved respondents sharing photographs that symbolise their survival while assimilating into Quebec's labour market, revealing that family, cultural groups, religious organisations, recreational activities, and professional networks play pivotal roles in their adaptation. These findings highlight the importance of a multifaceted support system in fostering survival and successful integration, suggesting that policymakers should focus on enhancing these networks to aid HSAIs’ adjustment to life in Quebec.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".