Family Income and Postsecondary Education In Canada
Bibliographic record
Abstract
We use data from the Surveys of Consumer Finance (1975-1993) to examine how postsecondary education participation rates have evolved over time and how certain variables may affect them. A number of socio- economic influences are shown to affect participation rates. Beyond these, particularly pronounced trend increases in postsecondary education attendance for children from low-income households have led to a convergence in the participation rates of children from different income groups and a consequent reduction in the regressivity associated with subsidies for postsecondary education. We consider possible reasons for this convergence. Conditioning on a number of other variables, we are particularly interested in the possibility that increases in family real income may have affected the demand for postsecondary education by children from low-income families more than the demand by children from high-income households. We find that, although income does have a statistically significant non-linear influence which can explain much of the cross-sectional difference in attendance at postsecondary institutions, its quantitative effects are not sufficiently strong to account for the convergence over time in participation by children from different family income groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".