Does the Rising Cost of Tuition Affect the Socio-Economic Status of Students Entering University?
Bibliographic record
Abstract
As tuition fees increase, universities need to be concerned whether costs have risen to a point where students from low-income families are being disproportionately excluded. Given the rates of increases in tuition fees in recent times, this outcome seems plausible and is often the opening point of discussions on this matter (see for example, the position of the Canadian Association of University Teachers, 2002, cited below). However, trends in university enrolments relative to trends in tuition fees would suggest otherwise. Consequently, we take as our starting point a review of what the existing studies have to say in this regard. We next review available data pertaining to the question of whether observed enrolment growth is attributable to increases in the proportion of high SES students. We found that the methodologies used and the time periods encompassed differ to an extent that the generalisability of the results necessarily need be constrained. The implication from our point of view is that we cannot be certain what the answer to the question would be for our University. Therefore, we devised a study using a novel methodology based on a national census data
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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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".