Two-in-One Diasporas? Comparing and Contrasting Migration Management in France and Canada
Bibliographic record
Abstract
How can two states claim the same diaspora? This issue is addressed in this comparative review of two models of public policy: Canada’s strategy for Francophone Immigration and France’s promotion of international mobility. Based on its century of expertise, Canada has developed a high profile migrant recruitment strategy that relies on networking activities for steering and engaging the Francophone diaspora. This strategy, carried out by government agents and professionals, has positioned Francophone migrants as ambassadors in charge of marketing Canada as a French-speaking hub. By contrast, France’s diaspora strategy is more dated: it is largely institutionalized, and aimed at protecting the rights and interests of its migrants, preserving national identity and ties, providing benefits and subsidies, and creating provisions for representation and vote facilitation. These policies are largely disconnected from Canada's strategy of boosting economic development, cultivating networks for engaging the global diaspora. The empirical data on Francophone promotion and recruitment in Quebec illustrate the extent to which a sophisticated diaspora strategy can transform imaginaries and reconfigure communities at both ends of the migration process. These findings point to the disruptive potential of contemporary diaspora strategies and call for more investigation into its political outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".