Intégrer pour exister ? Nationalisme sous-étatique et intégration des immigrés en Flandre et au Québec
Bibliographic record
Abstract
My PhD dissertation (Sciences Po Paris & Université Saint-Louis) considers the dilemma generated by immigration and diversity for political elites in two culturally and linguistic distinct sub-national communities: Flanders (Belgium) and Quebec (Canada). For such communities, immigrant integration represents both opportunities and challenges. Immigration might increase the relative demographic strength of the sub-national community yet, it might also weaken its cultural or linguistic cohesion. Focusing on the 1999-2014 timeframe and using discursive institutionalism, I ask how subnational elites respond to this dilemma. Using discourse analysis, I identify the position of members of regional parliaments and their rhetoric on four dimensions of immigrant integration (institutional, demographic, linguistic, and cultural). Contrary to other researches that have focused only on sub-nationalist and regionalist party positions (SNRP), my focus on political discourse and all elites allows me to show how ideas circulate and evolve through legislatures. My results run contrary to some expectations from immigration studies and federalism theory. I show that key arguments are shared between political elites when it comes to the linguistic, demographic and cultural dimensions of immigrant integration. Nevertheless and independently from the conceptions of integration put forward, I show that clear divergences remain when it comes to federal-subnational institutional arrangements for immigrant integration.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".