Higher Education Student Migration in Canada: Interprovincial Structure and the Influence of Student Mother Tongue
Bibliographic record
Abstract
Approximately ten percent of Canadian higher education students cross provincial boundaries each year to attend college or university. Despite its size and impact, the geography of higher education student migration (HESM) is not well documented in Canada. This paper analyses Statistics Canada’s Postsecondary Student Information System (PSIS) for the academic year 2016/17. Interaction matrixes are developed and mapped to analyse both the broad geographical structure of interprovincial HESM and the impact of language, specifically student mother tongue, in shaping migration patterns. In both the overall picture of HESM and that of mother tongue, HESM generates expected patterns as well as important migration variations. Ontario and Quebec anchor the national picture and, together with others, constitute exchanges amongst contiguous provincial clusters. Unique cross-country interprovincial connections are also revealed and migration by mother tongue generates further nuances still. The paper concludes with a discussion of the implications of this work for understanding (i) migration-based integration amongst Canadian provincial higher education systems, and (ii) with further research, the multi-scalar processes and geographies of HESM for institutional and local economic policy makers.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".