Do Transfers into the Community College Sector Graduate at the Same Rate? Evidence from Ontario, Canada
Bibliographic record
Abstract
The overwhelming majority of studies on transfer students have compared the outcomes of vertical transfer students (college-to-university) relative to “native” counterparts, referring to those that directly enter universities from high school and remain within them throughout the course of their studies. In turn, scholars have devoted only minimal attention to the outcomes of students that travel variable pathways into the community college sector (e.g. university to college). Through this study we attempt to correct such imbalance in the existing literature, leveraging various administrative files in Statistics Canada’s Education and Labour Market Linkage Platform (ELMLP) to analyze the pathway-based disparities in graduation rates among those students traveling various routes into the Ontario community college sector. Our logistic regression models reveal that students who transfer into the community college graduate at a rate that is 22 to 27% points lower than direct entries, and that these differences persist even after we control for student traits (e.g. age, sex), field of study, parental income, and familial characteristics (e.g. size, structure). Based on the observed findings, we theorize plausible mechanisms that could be suppressing the success of transfers into the community college sector and identify a series of potential strategies to ameliorate this situation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".