The demographic determinants of inter-provincial migration declines in Canada: A decomposition analysis
Bibliographic record
Abstract
Abstract This article studies the link between observed demographic changes and Canadian inter-provincial migration declines. Alongside recent changes in age composition, educational attainment, marital trends, and immigration, Canadian internal migration has widely been in decline. In this context, our project investigates the demographic determinants of Canadian provincial migration and the correlation between Canada’s socio-demographic shifts and the decline of inter-provincial migration. To do so, our analysis consists of two identical multivariate logistic regressions and an econometric decomposition using the data of the 1991 and 2016 Canadian Census Public Use Microdata Files. The dependent variable is inter-provincial migration, and the focal independent variables are age, educational attainment, marital status, immigrant status, and province of residence five years ago. By investigating how these determinants are associated with migration, we develop a greater understanding of how demographics predict inter-provincial migration in Canada, and how these demographics have changed and affected the overall decline in inter-provincial migration. In a broad sense, we examine the continuity of contemporary demographic trends in their relation to Canadian macro-economic human capital distribution. Through our investigation, we conclude that while compositional changes have some impact, it is a shift in the effects of these changes that largely explains the decline in Canadian inter-provincial migration.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".