Bibliographic record
Abstract
The number of lifetime international migrants worldwide has increased greatly in recent decades. Canada currently ranks as the fourth largest immigrant-receiving country with 8 million foreign-born residents in 2015. Most international migrants reside primarily in large metropolises, with more than 60 percent of Canada’s foreign-born living in the Toronto, Vancouver, and Montreal urban conurbations. This paper examines four challenges of global migration for Canada’s cities: housing and housing affordability, social services, employment, and integration and cohesion. The paper’s conclusion discusses implications for expanding our knowledge basis about global migration and cities of the future.Le nombre de migrants internationaux à vie a considérablement augmenté au cours des dernières décennies. Le Canada se classe actuellement au quatrième rang des pays d'accueil des immigrants avec 8 millions de résidents nés à l'étranger en 2015. La plupart des migrants internationaux résident principalement dans de grandes métropoles, avec plus de 60% des personnes nées à Toronto, Vancouver et Montréal. agglomérations. Ce document examine quatre défis de la migration mondiale pour les villes canadiennes: l'abordabilité du logement et du logement, les services sociaux, l'emploi, l'intégration et la cohésion. La conclusion du document discute des implications pour élargir notre base de connaissance sur la migration globale et les villes du futur.
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 | 0.001 |
| 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".