The Price of Knowledge: An Exploration of Student Demographics between Regulated and First-Degree Professional Programs in Ontario
Bibliographic record
Abstract
In recent decades, we have seen various governing bodies reduce their economic support for the publicly funded post-secondary education (PSE) system in Canada. This trend is one of the neoliberal measures which seeks to reorganize the structures and distribution patterns of public goods and services. Through the process of neoliberalization, the lack of public financing has created a funding gap for universities and colleges, which has been increasingly filled by relying on private sources of funding, primarily in the form of tuition fees. This shift has led to a rapid increase, and at times, deregulation of PSE tuition fees. In 2006, tuition differentiation assigned professional programs higher tuition fees than regulated program, revealing a new, reconstructed version of tuition deregulation. \nThis study seeks to explore the differences between student demographics of first-degree (undergraduate) professional and regulated programs in Ontario. A secondary data analysis using the 2018 National Graduates Survey (NGS) is conducted. Logistic regression models are used to predict the likelihood of professional or regulated program enrollment controlling for social markers such as source of funding, race/ethnicity, SES, gender, etc. An analysis of student debt is also performed to investigate the management of large PSE student loans. Findings reveal that students from more privileged and affluent backgrounds are more likely to be enrolled in first-degree professional programs, both nationally and in Ontario. The odds of enrollment for professional programs are higher for self-funded (not relying on student loans), non-racialized, Canadian-born-citizens, males, with high levels of parental education in Ontario. Additionally, students from marginalized groups are more likely to accrue high levels of student debt ($25,000 or more), take longer to repay their loans, and struggle with debt repayment. There is evidence to suggest that tuition differentiation may be functioning as an exclusionary policy that reproduces social inequities and class disparities. First-degree professional programs, which have higher tuition fees than regulated programs, are largely populated with students from affluent backgrounds. \nWhen examined cumulatively, these findings have implications for PSE policy and the \nability of PSE to function as a great equalizer. Since professional programs tend to lead to more affluent employment positions and higher wages, the cycle of economic marginalization may be reproducing itself through PSE.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".