Insertion sociale des immigrants iraniens en France et au Canada : Une étude comparative des effets des modèles universaliste et multiculturaliste sur l'expérience des individus
Bibliographic record
Abstract
This thesis analyzes the social integration of Iranian immigrants in France and Canada by comparing universalist and multicultural integration models. Inspired by the works of the Chicago School, particularly those of Georg Simmel and Robert Park, this research draws on the contributions of sociologists such as Danièle Lochak, Daniel Schnapper, Danièle Juteau, John Berry, Robert Gordon, Abdelmalek Sayad, François Dubet, Serge Weber, Haideh Moghissi, Nader Vahabi, Will Kymlicka, and other researchers who have explored migration dynamics and integration models. It relies on a qualitative approach combining semi-structured interviews, document analysis, and participant observation. The goal is to understand how institutional contexts influence migration trajectories and the challenges faced, particularly in accessing employment, education, and intercultural interactions. The study highlights that the French republican model favors an integration based on assimilation, valuing uniformity and neutrality in the public space, while the Canadian model officially recognizes cultural diversity and encourages its expression. However, this recognition does not necessarily guarantee effective social and economic inclusion, as institutional barriers and discrimination persist. These differences influence the experiences of Iranian migrants, who must adapt to varying expectations regarding integration and balance the maintenance of their identity with adaptation to dominant norms. The analysis highlights the adaptation strategies and identity tensions of immigrants according to migration policies and the social dynamics specific to each country. It also underscores the roleof community networks and interactions with the host society in the integration process. Iranian communities, though more structured in Canada than in France, do not always provide effective support in the face of professional and social challenges encountered by newcomers. However,the presence of a larger and better-established Iranian community in Canada mitigates the feelings of nostalgia and estrangement, thanks to the availability of Iranian businesses,restaurants, active social networks, and cultural events. These elements allow immigrants to maintain a link with their culture while integrating into Canadian society. However, despite these facilities, a certain sense of nostalgia and identity displacement persists. This research shows that integration is a complex process, going beyond mere adaptation to the host country's norms. It highlights the structural and individual challenges faced by migrants and opens up perspectives for future research on intercultural dynamics, socio-economic inequalities,and identity transformations in a migration context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".