Exploring university-to-college transfer in Ontario: A qualitative study of non-linear post-secondary mobility
Bibliographic record
Abstract
In this study, we explored the experiences of Ontario students who have engaged in a ‘reverse transfer’, i.e. moving from university-to-college (UTC). Data was collected through qualitative interviews with 20 participants who began their post-secondary journey in a university program, but left that program before completing it, and subsequently pursued a college program. We focused on the factors that led to the decision to reverse transfer, participants’ experiences and perceptions of the reverse transfer process, and what, if any, barriers they experienced. We found that motivations for leaving university were distinct from, though sometimes related to, motivations for pursuing college. Reasons for leaving university were clustered around three themes: academic struggles, mental/physical health/special education need struggles, and future prospects. These were highly interconnected and characterized by difficulties, ranging from mild to severe, coping with university. Motivations for pursuing college were more practical and straightforward, relating to subject interest, college learning environment, location, and future prospects. While most participants reflected very positively on their decision to transfer, there were some negative or ambivalent reflections about having left university before completing their degrees. These were largely related to a sense of personal failure and/or the negative reactions of others, particularly parents. Personal and external (usually parental) negative reflections were tied to cultural and societal expectations about high achievement and perceptions of university education as superior to college education. We conclude with policy recommendations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".