Do Subnational Agreements Induce\nInterprovincial Migration? Empirical\nEvidence from Canada’s Aggregate\nMigration Patterns
Bibliographic record
Abstract
Certains gouvernements provinciaux au Canada ont négocié des ententes visant à lever les barrières migratoires interprovinciales. L’auteur s’intéresse à l’évolution de l’étendue et de la configuration de ces barrières dans le temps et se penche sur l’incidence des ententes négociées à l’aide d’un modèle gravitaire et de données de panel sur les flux migratoires interprovinciaux entre 2000 et 2015. Selon son appréciation, ces barrières sont nettement plus importantes que ne le laissaient supposer les estimations antérieures. Observation plus importante encore, rien ne confirme que les ententes aient réduit les entraves à la migration. Abstract: Some provincial governments in Canada have negotiated agreements to try to remove barriers to interprovincial migration. I estimate the extent and pattern of these barriers over time and study the effect of the agreements using a gravity model and panel data on interprovincial migration flows during 2000–2015. My estimates indicate that these barriers are remarkably larger than previous estimates. More important, there is no evidence that the agreements have reduced the barriers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".