Bibliographic record
Abstract
Toronto is the most popular destination for people migrating from India to Canada. According to Statistics Canada, roughly half of the Indian-born population of Canada lives in the greater Toronto area. Over time, Indian-focused associations, businesses, and services were established in Toronto, making the city an attractive destination for subsequent cohorts of Indian migrants. While Indian migration to Toronto has held steady, in recent years, Indian migrants have also been moving to non-metropolitan destinations outside of Toronto, where there has not been a long-standing history of Indian migration. This is a result of new immigration policies in Canada that actively encourage international migration to small- and mid-sized cities as well as a large increase in the number of international students attending colleges and universities outside major centres in recent years. While numerous academic studies have addressed Indian migration to Toronto, very little is known about Indian migration to non-metropolitan destinations. This chapter aims to address this gap. The chapter draws on census data to illustrate how Indian migration to small- and mid-sized cities in northern Ontario has grown over the last ten years. It then turns to an analysis of information collected through semi-structured interviews carried out with key informants in Thunder Bay, a city in the region that has experienced a major uptick in Indian migration. The interviews shed light on how Thunder Bay has evolved to be an emerging destination for Indian migrants, and how Indian migrants have experienced life in this northern mid-sized city.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".