Bibliographic record
Abstract
Immigration has been an important characteristic of the Canadian society for hundreds of years as it has often been used as a tool to maintain the demographic trends in the country. Historically, most immigrants have chosen to migrate to urban areas, especially the three metropolitan cities: Toronto, Montreal and Vancouver. Immigrants make this choice due to a variety of reasons including job opportunities, social networks, family etc., all of which are perceived as abundant in urban centres. However, the current state of rural areas in Canada has created a need for attracting and retaining immigrants. I would like to focus in rural Ontario which, like most rural areas in the country, is experiencing a relative decline in population due to out-migration of youth and an ageing cohort of baby-boomers. With continuing low birth rates, rural Ontario will have to rely on transforming communities to become more attractive for immigrants. Through my research I present an exploratory case of immigrants who are currently living in Bruce and Grey county. The research delves into unique stories of individuals- their successes and challenges by painting a picture of the life of an immigrant in a rural Canadian town.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".