Bibliographic record
Abstract
My partner Zoë was killed in October 2013. We worked together as arts educators, mostly with people involved in the Canadian mental healthcare system. This thesis explores social conceptions of madness, drawing on theorists such as Tobin Siebers, Sara Ahmed, Lynne Huffer and Ann Cvetkovich, and engaging with works of art by people who have been involved in mental healthcare in some way. There is a simultaneous exploration of my process of grieving Zoë's death, drawing on the tradition of autocritique by writers such as bell hooks, Eve Kosofsky Sedgwick, and others. Chapter one looks at poetry produced by the Workman Arts Group and a zine by Anna Quon, investigating the impact of diagnoses of mental illness on the reception of art and artists, as well as the history of silencing and confinement of mad bodies. Chapter two explores the memoirs of Bobby Baker and Merri Lisa Johnson, emphasizing the impact of diagnosis on those not already marginalized by society, and drawing attention to the kinds of communities that memoirs produce, as well as the connection between community, capitalism, and the grievability of life. Chapter three looks at the paintings, performance art and installations of Yayoi Kusama to complicate the connection between madness and celebrity power, as well as Kusama's own engagement with death and infinity. I conclude by looking briefly at the deaths of Michael Brown and Robin Williams, and again at my own grief one year after Zoë's death.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".