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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".