Short‐term relocation versus long‐term migration: Implications for economic growth and human capital change
Bibliographic record
Abstract
Abstract Driven by the growth of Canada's resource sector, interprovincial employees (IPEs), or individuals who work in one province and reside in another, have emerged as the main source for interprovincial worker mobility within Canada, with their numbers far exceeding the number of interprovincial migrants (individuals who permanently relocate from one province to another) on a yearly basis. As such, IPEs represent a significant number of workers and play an increasingly important role in the Canadian labour market, enabling individuals to respond to skill shortages and job opportunities over both the short and long term. This paper contrasts these two groups using a number of measures commonly used to characterise interprovincial migration. Results reveal that although the two groups are broadly similar, there are also subtle differences between the two groups, including differences in the age migration schedule and other sociodemographic characteristics of IPEs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".