Reversing the trend: lessons learned from young in-migrants in two rural communities in Nova Scotia
Bibliographic record
Abstract
Taking its lead from calls to change attitudes of Nova Scotians, this research explores the motivations, experiences, and contributions of young people who are bucking the trend of youth out-migration and rural population decline, and choosing rural lifestyles. Looking beyond the migration decision to what has happened since the move, migrants reveal opportunities to leverage existing human and social capital and to attract and retain young people. Connections between youth and community wellbeing have been identified through the recognition of youth out-migration as a symptom and cause of rural decline, and the presence of young people as an indicator of community success. While the economic impact of in-migrants has been studied in various contexts, their potential holistic contributions to wellbeing warrant further research. This research found that young people were aware of their importance to the future of the community in maintaining services such as local schools, replacing aging volunteers, and bringing the energy of youth more broadly. This presentation will provide an overview of the results of this research from two communities in Nova Scotia, as well as potential lessons learned and next steps for policy makers, community members, and researchers in attracting and retaining young people in rural communities.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".