Bibliographic record
Abstract
RURAL-TO-URBAN COMMUTING AS A MEAN FOR RURAL-URBAN INTERDEPENDENCE? The aim of this paper is to examine the extent to which rural regions benefit from closeness to large cities. It is often argued that rural regions can enjoy urban-based growth through urban sprawl, i.e. deconcentration of urban activities to rural regions. Analyses of rural regions in e.g. Canada show that thriving and prosperous regions (in terms of employment and population growth) often are located within commuting distance to urban centers (e.g. Partridge et al 2010). This suggests that one mechanism behind these patterns involves the process of people moving to the countryside while working in the city (Betrand & George-Marcelpoil 2005), which put rural-to-urban commuters at the center of attention (cf. Goetz et al 2010). Even if this type of commuting flows are prime examples of rural-urban integration and a main way in which rural regions may enjoy urban spread effects, there are few analyses that directly examines rural-to-urban commuting (Partridge et al 2010). Many key issues are not fully analyzed. For instance; what are the critical distances for urban spread effects through rural-to-urban commuting? To what extent do they differ across different categories of the labor force and across different sectors? In this paper, these questions are examined using detailed Swedish longitudinal matched employer-employee data. The main results show that regions close to urban centers experience positive spread effects from closeness to large cities. Evidence shows that rural population growth from urban sprawl is a key determinant behind rural-to-urban commuting. Using the methodology developed by Partridge et al (2010), the paper also estimate how distance to nearest urban center influence commuting patterns; thereby capturing the rural-urban interdependence. A novelty in the analysis is that critical distances where urban spread effects for rural regions vanishes are estimated for different parts of the labor force with respect to education level and occupation. The main results show that there exist small differences in distance between these groups. REFERENCES Betrand, Nathalie and George-Marcelpoil, Emmanuelle (2005), "Residential Growth and Economic Polarization in the French Alps: The Prospects for Rural-Urban Cohesion" in Hoggart, Keith (2005), The City's Hinterland: Dynamism and Divergence in Europe's Peri-Urban Territories. Goetz, Stephan., Yicheol, Han., Findeis, Jill. and Brasier, Kathryn (2010), "U.S. Commuting Networks and Economic Growth: Measurement and Implications for Spatial Policy", Growth and Change, vol. 41 Partridge, Mark., Ali, Kamar. and Olfert, Rose (2010), "Rural-to-Urban Commuting: Three Degrees of Integration", Growth and Change, vol. 41
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".