Immigrant Voices: An Exploration of Immigrants' Experiences in Rural Ontario
Bibliographic record
Abstract
Immigration has been an important characteristic of Canadian society for years as it has often been used as a tool to maintain and grow population. In recent history, most immigrants have chosen to migrate to urban areas, especially the three metropolitan cities: Toronto, Montreal and Vancouver. However, the current state of rural areas in Canada has created a need for attracting and retaining immigrants. For instance, rural parts of Ontario are experiencing a relative decline in population due to out-migration of youth and an ageing cohort of baby-boomers. Challenges in maintaining population growth and revitalising the economy has reignited the discussion about attracting immigrants to communities outside the urban core. This project started out of interest in finding out about the experiences of skilled migrants who are currently residing in rural Ontario. It presents an exploratory case of a small number of immigrants who have been living in the Bruce-Grey area for less than 10 years. These unique individual stories delve deep into the successes and challenges, the barriers and opportunities faced by an immigrant living in a rural Canadian town.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.050 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".