Bibliographic record
Abstract
ABSTRACT As Canada increases immigration rates, there is a greater need for geographic dispersion to counteract issues of population ageing and economic disparities. Historically, Canada's main Census Metropolitan Areas (CMAs) have experienced the most significant gains in terms of new arrivals. The problem, however, is that this leaves rural regions falling behind in terms of both population increases and overall development. As such, understanding the characteristics of rural movers is of utmost importance, especially regarding potential policy initiatives aimed at ensuring newcomers to Canada are evenly distributed across the country. This study adds to the growing body of literature looking at the urban‐rural divide by investigating the characteristics of individuals who engage in rural migration, including secondary migrants, by looking at those who lived in urban Canada in 2020 but, as of 2021, have moved into rural locations through the use of the 2021 Canadian Census. Overall, individuals making migratory decisions are often white, married, with children, and non‐immigrants, thereby necessitating updated initiatives as a means of drawing in a more diverse newcomer population to rural destinations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".