ERIC ED499883: Who Gets What? The Distribution of Government Subsidies for Post-Secondary Education in Canada. Canadian Higher Education Report Series
Bibliographic record
Abstract
This study is one part of a two-part inquiry into subsidies for post-secondary education in Canada. Governments in Canada spend over $4 billion each year in transfers to individuals for the purpose of post-secondary education. Roughly half of this money goes out in need-based loans and grants, while the other half goes in "universal" benefits to which all are entitled, such as tax credits and the Canada Education Savings Grant. Based on a combination of administrative and survey data, the study estimates the distribution of these two forms of assistance by family income quartile. The study shows that need-based assistance is only lightly progressive; 40% of all assistance goes to students from families with above-median incomes. "Universal" assistance is outright regressive, with over 62% of assistance going to students from families with above median incomes. As a result, the overall skew in combined need-based and universal assistance is slightly regressive. Given the known problems in access for low-income students, this skew is inconsistent with a strategy to help low-income families. An appendix also examines the distributional effects of the major hidden subsidy to students, which is the indirect subsidy to tuition fees implicit in government subsidies to institutions. The examination finds that these subsidies, too, are highly regressive and that a fee-reduction approach to improving access will in fact aggravate the overall problem of too many subsidies going to high-income families. (Contains 19 footnotes, 13 figures and 9 tables.) [This document was published by the Educational Policy Institute (EPI).]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.109 | 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 teacher head, 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".