HOW DO STUDENTS CHOOSE A UNIVERSITY?: An Analysis of Applications to Universities in
Bibliographic record
Abstract
This study uses a unique set of microdata on university applications to examine the role played by institutional attributes in choices made by graduating high school students between the 17 universities in the Province of Ontario, Canada. We estimate a rank-ordered logit model that uses all information contained in each applicant’s ranking of institutions. Applicants prefer universities that are closer to their homes, spend more on scholarships and teaching, and offer higher levels of non-academic student services. Smaller class sizes are preferred by female applicants but not by males. High levels of research activity discourage applications. Smaller, primarily undergraduate institutions suffer from a low placing in the annual national university rankings but larger universities do not. KEY WORDS: rank-ordered logit; university choices; school characteristics; Canada. University education in Ontario, Canada’s largest province, is delivered through 18 publicly funded, degree-granting institutions (only 17 existed at the time the data used in this paper were generated). Although they vary considerably in size, from slightly more than 5000 students in 2003 to
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".