Bibliographic record
Abstract
As rural communities struggle with the out-migration of their youth and an ageing population, they are increasingly looking to immigration as a partial solution to their dilemmas of a shrinking tax base and labour market shortages. There has been a great deal of research on immigrants, but little in the way of rural research and what rural research there is has tended to examine how to attract immigrants to rural and small communities. There has been very little research that has examined the rural immigrants’ experience, a gap this study begins to address. This is a qualitative study that examines the experiences of 12 immigrants who are now residing in rural communities in Ontario. The study reports back on three themes: employment issues, social issues, and cultural issues. Keywords: rural, immigration, employment, social issues, cultural issues ----------------------------------------------------- À travers leurs yeux: expériences d'immigrants ruraux Résumé Alors que les communautés rurales doivent faire face à l'exode des jeunes et à une population vieillissante, elles considèrent de plus en plus l'immigration comme une solution partielle aux dilemmes de la réduction de l'assiette fiscale et de la pénurie de main d'œuvre. Il y a eu beaucoup de recherches sur les immigrants, mais très peu de recherches en milieu rural et les recherches effectuées en milieu rural ont eu tendance à examiner la manière d'attirer les immigrants dans les petites communautés rurales. Très peu de recherches ont examiné l’expérience des immigrés ruraux, une lacune que cette étude commence à combler. Il s'agit d'une étude qualitative qui examine les expériences de 12 immigrants qui résident maintenant dans des communautés rurales de l'Ontario. L'étude fait un rapport selon trois thèmes: les problèmes d'emploi, les problèmes sociaux et les problèmes culturels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".