Social Capital, Labour Markets, and Job-Finding in Urban and Rural Regions: Comparing Paths to Employment in Prosperous Cities and Stressed Rural Communities in Canada <sup>,</sup>
Bibliographic record
Abstract
This paper compares paths to employment (job-finding) in prosperous cities and economically-stressed rural communities in Canada. Since the pioneering work of Mark Granovetter (1973; 1974) , sociologists have investigated the role of social capital in job-finding (specifically, the use of strong and weak social ties to find out about employment opportunities). To date, however, there have been few direct comparisons of job-finding in urban and rural settings (see Lindsay et al., 2005 ; Wahba and Zenou, 2005 ). Using data from two major surveys and a qualitative interview project, we uncover several important differences in urban and rural paths to employment. First, we find that both strong and weak ties are used more frequently by rural residents to find a job, while city-dwellers rely more often on formal or impersonal means. Second, we find much stronger evidence of differentiation within rural regions. Long-time rural residents are much more likely to use strong and weak ties to find employment than are newcomers. However, rural residents who used weak ties as paths to employment have significantly lower incomes. None of these patterns are evident in the cities. Together, these findings lead us to conclude that job-finding in rural settings is strongly affected by constraints – in the labour market and in social capital resources – that are not present in cities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".