Bibliographic record
Abstract
Curative Illnesses first took shape as an undergraduate term paper in a Québécois literature class.Well over a decade, two continents, and three universities later, my musings on how nations conceive of themselves and how discourses of illness, health, and pathology intervene in and shape ideas about nationhood have taken on a different form thanks to the instruction and assistance I received from countless people.Teachers and mentors over the years, including Jarrod Hayes, Frieda Ekotto, Martin Pernick, Katherine Ibbett, David Caron, Victor Laurent-Tremblay, and Catherine Black provided the guidance I needed to turn a short essay into a book and naive ideas into ones that are (hopefully) less so.Jennifer Metsker reminded me that the most helpful words when trying to pinpoint gaps in my own logic will always be "I don't understand."Colleagues at the University of Technology, Sydney's Transforming Cultures Research Centre, and in the Cultural Studies Group read drafts, gave encouraging feedback, and offered friendship and moral support in the final phases of the project.Finding such a collaborative and supportive academic home is what young scholars can only hope for.Special thanks go to Sara Wellman, who convinced me that having the right research assistant truly does make all the difference, and who read drafts even before she took on this formal role.Having received so much help along the way, any errors or oversights that remain in the text can only be my own.I also owe my thanks to the various organizations and agencies that funded this research: the Social Sciences and Humanities Research Council of Canada, the Horace H. Rackham School of
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.321 | 0.158 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".