Dependencyat aDistance: Implications of Workforce Mobility for Community Resilience Part
Bibliographic record
Abstract
The development of new transport technologies and infrastructure has facilitated increased mobility across globe, including of goods, finances and people; so much so that, in the present, it has been argued that a new mobilities turn has emerged within social science research (Hannam et al. 2006). Changes in mobility patterns over time are particularly pronounced in the study of mobility for work, also known as employment-related geographical mobility (E-RGM). E-RGM involves the movement of workers across municipal, provincial, or national boundaries to and from their place of employment and place of residence (Temple et al., 2011). For many communities E-RGM contributes to community survival and resilience by providing a important source of local earnings and an alternative to outmigration. E-RGM, particularly long distance commuting, also creates a particular way of life for residents who are ‘gone away’ for work and for others in the communities where they live and/or work. Research on E-RGM has identified both opportunities and challenges for workers, their families, and source (home) and host (work) communities (Vodden and Hall 2016). One implication of increased E-RGM is new forms of dependency on work in distance locations, with employment opportunities shaped by decision-makers, financial flows and market trends that are distant from workers’ source communities (Storey and Hall, forthcoming). Organized in conjunction of the On the Move Partnership, this session will explore the impacts of E-RGM and “dependency at a distance” for rural and urban communities as well as existing and potential responses to associated challenges and opportunities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".