Socio-Professional Integration of Recent Immigrants in Quebec: The Role of Information and Social Networks
Bibliographic record
Abstract
The goal of massive immigration in Canada and in Quebec is essentially to thwart the aging active population and labour shortages. Like Canada, Quebec has adopted a policy of economic immigration where most people of selected are young, have relatively high education levels and speak French and/or English. However, professional integration has been more difficult for recent immigrants compared to previous cohorts. Many studies including government reports associate socio-professional integration difficulties with language barriers, the non-recognition of achievements and skills, discriminatory practices and the lack of social networks. These often mention the importance of having the pertinent information, that helps newcomers integrate the labour market quickly and in a way that corresponds to their expectations. The goal of this article is to reflect on the nature, role and impact of informational flows conveyed by different social networks on socio-professional integration from the beginning of the migratory project, the arrival in Quebec. Studies that analyze the impact of social capital on socio-professional integration focus on structural characteristics of information networks rather than on the quality of the relations themselves. Yet, the nature of these relations influences the behaviour and strategies of individuals who receive information. What information is essential to successful socio-professional integration? What are the factors that can accentuate informational gaps? How can informational flows circulated by different networks influence immigrants’ socio-professional integration processes? To answer these questions, we shall first describe the main difficulties faced by recent immigrants in Quebec. Then, the article analyses informational fluxes that orient immigrants’ social and professional integration processes, and rethinks the role played by social networks in the circulation of information. Finally, the conclusion suggests further research orientations for immigration and integration public policy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".