May Luong is with the Labour and Household Surveys
Bibliographic record
Abstract
nterest in student loan debt heightened in the early 1990s when the average tuition fees jumped by 10% two years in a row. While the rate of tuition increase subsequently fell back to single digits, between 1989/1990 and 2008/2009 tuition fees more than doubled in constant dollars (Chart A).1 The rise in tui-tion fees in most provinces brought increased atten-tion to levels of student borrowing and associated debt loads. One study found that between 1982 and 1995, the proportion of bachelor’s graduates with student loan debt rose from 45 % to 47 % for men and from 39 % to 44 % for women. Average loan amounts at graduation for those with a bachelor’s degree also rose during this period by 121 % for men and 145 % for women (Finnie 2002). The rise in average tuition fees is the result of a sub-stantial shift in the funding of postsecondary educa-tion (PSE), a change requiring students to pay proportionally more while governments pay propor-tionally less (Schwartz and Finnie 2002). Between 1989 and 2009, average tuition fees as a percentage of total revenues for universities and colleges more than dou-bled, rising from 10 % to 21 % while funding from government fell from 72 % to 55%.2 Although the cost of postsecondary education has increased for students, most individuals interested in pursuing studies are able to do so,3 whether through personal savings, parental contributions or govern-ment-sponsored student loans (see Canada Student Loans Program). For those not eligible for government-sponsored programs, loans through private institutions are also available. It is widely accepted that borrowing for postsecond-ary education is a long-term financial investment. Indi-viduals spend time and money on their education to increase the chances of obtaining meaningful, higher-Chart A Average tuition fees for full-time undergraduate university students Source: Statistics Canada, Tuition and living accommodation costs for full-time students at Canadian degree-granting institutions,
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.146 | 0.063 |
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".