Bibliographic record
Abstract
The metaphor of the 'body politic' is an apparently optimistic one, as it is supposed to signal the relationality and connectedness of all members of the whole. This paper argues that the metaphor does not deliver on its potential because it associates illness and disability with lack of quality of life. I argue that the embodied experience of chronic illness or disability can inform political virtues that would be salutary for a body politic confronting a health-based crisis such as the COVID-19 pandemic, and which challenge the assumptions of the body politic as traditionally constituted. I begin by drawing on scholarly treatments of the body politic to demonstrate that the body has consistently been idealized as able-bodied, and illness and disability are framed as inconsistent with quality of life or the functioning of the whole. In part two of the article, I draw upon Eli Clare's autoethnographic reflection entitled The Mountain as a starting point for re-imagining the body politic as disabled or chronically ill. I propose two political virtues that might be drawn from such a re-imagining: reflexivity and the recognition of limits. In part three, I argue that in the context of the COVID-19 pandemic, during which disability and chronic illness are foregrounded as important political issues, disabled and chronically ill people offer key insights, advocating a politics of relationality that has its starting point in their own disabled or chronically ill bodies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".