Where Did They Go? Regional Patterns in Early Transfer in Ontario Post-Secondary Education
Bibliographic record
Abstract
Research on transfer student flows has focused almost exclusively on transitions occurring between colleges and universities. Few have sought to systematically examine the regional dimensions of these student flows, and how they may map on to prevailing migration patterns that drive individuals out of remote geographical regions. Through this study, we perform the first comprehensiveanalysis of regional dynamics in early transfer student flows within Ontario post-secondary education (PSE), drawing on novel administrative linkages within Statistics Canada’s Education and Labour Market Linkage Platform (ELMLP). Our empirical analyses (i) map the magnitude of transfer student flows across the province, and (ii) statistically model the predictors of within- and cross-region forms of student transfer. Our findings demonstrate that PSE students commencing their studies in the provincial north are more likely to transfer out of their region, and that correcting these imbalances could serve as a useful strategy to retain and inject further human capital into northern communities. We explore the implications of these findings for both provincial policy makers and researchers interested in how geography shapes student trajectories.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".