Talk, Body, Performance: Mental Health Rhetoric in Corporate, Government, and Institutional Settings
Bibliographic record
Abstract
Rhetorical studies in health and medicine often point out the ways in which medical empiricism is structured as an arhetorical entity. This dissertation delves into a rhetorical analysis of psychiatric illness through a study that considers how rhetoric informs how mental health is viewed, treated, and embodied in the present-day Canadian context. This study uses a combined methodological approach, merging classical concepts of rhetorical analysis from Aristotle with more contemporary conceptual theories by Kenneth Burke to Michel Foucault, within a disability studies framework. This approach is applied to examine how mental illness is rhetorically structured in corporate, government, and institutional settings. The major campaigns informing this study include the Bell Let’s Talk campaign, the Government of Canada’s E-Health initiative, Better Health Together, the institutional response to student suicide at the University of Waterloo, and Queen’s University's Jack Talks campaign. By bringing together various mental health campaigns that purport to end stigma, treat mental health, and work towards a mentally “healthier” society, this study seeks to formulate a framework that students and teachers can use to rhetorically assess mental health discourse without resorting to what Robert Crawford would call ‘healthist’ assumptions while concurrently encouraging the formulation of non-discriminatory practice. This dissertation argues that the mental healthcare campaigns call forth very specific forms of “talk,” performativity, and embodiment that shape, limit, and constrain the ways in which psychiatric disability is treated within a Canadian context. Through a rhetoric of self-care, healthcare is depoliticized and individualized; a constrained conceptualization of “good” mental health is shaped through corporate, government, and institutional campaigns.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.078 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".