Proximity, Prosperity, and Participation: Examining Access to Postsecondary Education among Youth in Canada's Provincial North
Bibliographic record
Abstract
Abstract Despite increased proportions of students attending some form of postsecondary education (PSE), some social groups continue to face significant barriers and encounter difficulties making the transition. Previous research suggests that proximity to postsecondary institutions may have a positive impact on PSE participation rates, even more so for lower‐ and middle‐income families. We know comparatively less, however, about how these processes operate for social groups in varied social and economic contexts. In Canada, many highly ranked, prestigious universities and colleges (with greater arrays of fields) tend to be clustered in large, urban centers, and in the southernmost parts of Canadian provinces, but it is not clear whether these differences impact PSE outcomes. Drawing on cycles 1 to 4 of Statistics Canada's Youth in Transition Survey (YITS−Cohort A), our findings reveal that individuals from the northernmost parts of Canadian provinces do experience difficulties accessing various types of PSE (and in the timing of doing so). Moreover, our results suggest that much of these location effects are attributable to not only economic differences in terms of parental income but also cultural and dispositional differences related to parental education and their aspirations for their children's education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".