Rural Youth: Stayers, Leavers and Return Migrants
Bibliographic record
Abstract
There has been for some time substantial concern regarding the loss of young people in rural communities. There is a sense that most rural communities offer few opportunities for their younger people, requiring them to leave for urban communities, most likely not to return. While there is a considerable body of research on interprovincial migration, relatively little is currently known about migration patterns in rural and urban areas in Canada. According to our analysis, in virtually all provinces young people 15 to 19 years of age are leaving rural areas in greater proportions than urban areas - in part to pursue post-secondary education. While there are more complex migration patterns affecting the 20-29 age group, the net result of all migration is that the Atlantic provinces - as well as Manitoba and Saskatchewan - are net losers of their rural population aged 15-29. The problem is particularly acute in Newfoundland. In the Atlantic provinces, rural areas which fare worse than the national average - in terms of net gains of youth population - do so not because they have a higher than average percentage of leavers but rather because they are unable to attract a sufficiently high proportion of individuals into their communities. Of all individuals who move out of their rural community, at most 25% return to this community ten years later. The implication of this result is clear: one cannot count on return migration as a means of preserving the population size of a given cohort. Rather, rural areas must rely on inflows from other (urban) areas to achieve this goal. Some rural communities achieve this; that is, they register positive net in-migration of persons aged 25-29 or older, even though they incur a net loss of younger people. Individuals who move out of rural areas generally experience higher earnings growth than their counterparts who stay. However, it remains an open question in which direction the causality works: is the higher earnings growth the result of the migration process itself or does it reflect the possibility that people with higher earnings growth potential are more likely to become movers?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".