On the Move: Community Impacts of Long Distance Commuting – Summary Report for Mackenzie, BC.
Bibliographic record
Abstract
Labour mobility presents numerous opportunities and challenges for communities in rural and small town settings. Opportunities exist in terms of filling much needed service and worker demands, while also presenting the possibility for community growth and stability by attracting new residents and businesses. However, the challenges associated with integrating migrant and transient workers into the fabric of community and maintaining consistent service and worker delivery presses the limited capacity available in rural and small town communities. The purpose of this research is to provide an integrative lens through which to view labour - community dynamics within the On the Move: Employment-Related Mobility in the Canadian Context(E-RGM) project in northern BC. This seven year national project about labour mobility is spearheaded by Memorial University, with over 40 researchers from 22 universities (www.onthemovepartnership.ca). In northern BC, our research is premised on a foundation of comparative community/regional research investigating the differences and similarities in preparation for and management of mineral resource activity in two case study communities/regions: Williams Lake and Mackenzie.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".