Trends and Effects of Privatization on Universities in Canada
Bibliographic record
Abstract
With the rise of privatization of universities across Canada in the last decade, it has become increasingly important to understand its impact on students both financially and on the universities themselves. While many previous papers analyze trends or the benefits/disadvantages of privatization of public education, this paper focuses on trends of funding sources in universities, in particular, government spending, student tuition, increased private donations, and student loans. Specifically, this paper will delve into the trends over time, particularly focusing on the recent last two decades. The main conclusion to be reached by this paper is a look at privatization through the lens of a decrease in government spending and increased presence of corporate sponsorship, changes in tuition, and changes in overall student debt. Using data from public sources as well as past papers and analyzing these have led to several conclusions. Public spending has decreased through reduced government spending as a percentage of operating revenue. This in turn has led universities to increase the need for other sources of funding, more notably, through tuition and private donations from individuals or corporations. Both these sources have increased as a source of funding overtime. Moreso, while domestically tuition has grown at a more or less normal rate, international student tuition has skyrocketed. Next, it has been shown that increased tuition has direct effects on student debt which has also been shown to increase overtime. Lastly, private donations and non-governmental grants from both individuals and corporations have increased significantly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".