The Places We'll Go: Rural Migration in Canada
Bibliographic record
Abstract
As Canada increases immigration rates, there is a greater need for geographic dispersion to counteract issues of population aging and economic disparities. Historically, Canada’s main Census Metropolitan Areas (CMAs) have experienced the greatest gains in terms of immigrant recruitment and retention. The problem, however, is that this leaves rural regions falling behind in terms of both population increases and overall development. As such, understanding the characteristics of both rural movers and residents is of utmost importance, especially in regard to potential policy initiatives aimed at ensuring newcomers to Canada are evenly distributed across the country. This study adds to the growing body of literature looking at the urban-rural divide by investigating the characteristics of rural migrants, an important component of which are secondary migrants, who lived in urban Canada in 2015 but, as of 2016, have moved into rural locations through the use of the 2016 Canadian Census, as well as those of residences within these locations in both 1991 and 2016 through the use of the Census for each year. Overall, individuals making rural migratory and residential decisions are often married, with children, and of non-visible and non-immigrant status, thereby necessitating updated initiatives as a means of drawing in a more diverse newcomer population to rural destinations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".