Rural‐to‐Urban Commuting: Three Degrees of Integration
Bibliographic record
Abstract
ABSTRACT Commuting ties between rural places of residence and urban places of employment are among the most visible forms of rural–urban integration. For some rural areas, access to urban employment is a key source of population retention and growth. However, this access varies considerably across rural areas, with distance representing a primary deterrent. In addition to distance, the size of the urban community will also influence rural‐to‐urban commuting opportunities. In this paper, using Canadian data, we empirically estimated the influence of local rural population and job growth on rural out‐commuting within the urban hierarchy. We find consistent support for the deconcentration hypothesis where population moves to rural areas for lifestyle and quality of life reasons, while retaining urban employment. Further, we find some evidence that in addition to distance from the nearest urban center being a deterrent, increased remoteness from the top of the urban hierarchy exerts a positive influence on out‐commuting. Recognition of these types of rural–urban linkages through commuting is essential in designing Canadian rural policy and targeted programs that may effectively support local rural populations. In particular, they point to the need to have reasonable transportation infrastructure for urban accessibility, which should be complemented by other “built” infrastructure to improve the livability of rural communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".