Determining Demand for University Education in Ontario by Type of Student
Bibliographic record
Abstract
We specify and estimate a demand equation for university education in Canada that is a function of tuition fees, real disposable income per capita and other variables that capture a student’s opportunity cost. Our model has a number of novel features. We utilize application data, rather than enrollment data, due to the disequilibrium nature of Canada’s university system. We also disaggregate demand into demographic components: male and female, secondary school applicants and ‘‘other’’ applicants, and type of university. A last novel feature is the use of the Maclean’s university rankings as a determinant of demand. Our results suggest that the demand functions differ across the demographic characteristics in sensible ways. Broadly speaking, male applicants tend to be more price sensitive than females and tend to exhibit stronger income effects. Students applying from high school do not object to paying for a quality education, whereas ‘other’ students tend to be more discriminating on price. In most cases, an improvement in a university’s ranking exerts a positive influence on the number of applications received.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".