Untitled Item“Enough is Enough”: A Critical Discourse Analysis of a ‘Mental Health Crisis’ on Campus
Bibliographic record
Abstract
This major research paper is a critical discourse analysis (CDA) of ten news articles covering a series of suicides and ‘mental health crises’ at one Ontario university in 2019. The lack of support/responses led to student protests and institutional maneuvers. This CDA highlights these varied ways of responding and talking about ‘mental health’ as well as the lives of students affected and who spoke out. It also takes up institutional responses such as the physical barriers built in “suicide hotspots” and deferral of blame. Through the lenses of Mad Studies and Disability Justice, my findings point to various discourses that compete for ‘attention’ as well as confirmation of the discursive dominance of medical-institutional approaches and their power. It also highlights how much injustice there is when it comes to disability.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".