‘Stand by me’: competitive subnational regimes and the politics of retaining immigrants
Bibliographic record
Abstract
This article examines the pivotal role of immigration for subnational units, focusing on their efforts to retain immigrants. In the global competition for ‘wanted’ immigrants, subnational governments have developed innovative immigration-related programmes tailored to their demographic, economic, and linguistic needs. While previous research has mainly focused on the selection of international immigrants, this study delves into subnational efforts to keeping immigrants ‘in’. Using Canadian provinces as a case study, and through a qualitative content analysis of policy documents and immigration schemes, this article asks: To what extent do Canadian provinces compete to retain immigrants? The analysis reveals ‘subnational competitive immigration regimes’, wherein subnational units take policy actions to not only attract immigrants but influence or hinder their internal mobility. Downstream, provinces employ settlement and integration services to encourage retention. Upstream, provinces fine-tune their selection streams to choose migrants who are deemed more likely to stay. Policy outputs reveal how ‘wanted immigrants’ are conceptualized at the subnational level: those who can demonstrate a commitment to staying, rather than solely those who fulfil economic and demographic needs. The results shed light on the interplay between international immigration policies and immigrants’ internal mobility, and emphasize the significance of subnational policy in retaining immigrants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".