Auditing the Illness: Critical Analysis of Emergent Higher Education Mental Health Policy in Major Ontario Universities
Bibliographic record
Abstract
Since tragic on-campus suicides like those of Elizabeth Shin, there has been a call from the community for sensible mental health policies to be developed at Canadian universities. Mental health policies in Ontario universities are still in development and what is currently being used in place of dedicated policy documentation is often cold and legalistic, or simply inappropriate for use with mental health issues. The research surrounding mental health policy in higher education is limited, as the issue of mental health in policy appears to have only recently become a point of discussion. In this study, I attempt to create that discussion, addressing legalistic and neo-liberal trends in policy. To this end, I compiled the developing frameworks and existing policies from 13 major universities across Ontario (i.e., the institutions with more than 10,000 students) and examined them for precisely these neo-liberal trends. I conclude by arguing that current procedures for handling mental health issues (including the use of student codes of conduct and no-harm contracts) are not humanistic but, instead, bureaucratic. I also note that some of the currently developing mental health policies show many of the same tendencies. I caution policy makers to consider a more humanistic approach to mental health policies if on-campus tragedies are to be avoided.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".