Epilogue: Ethics, Aesthetics, and Poiesis
Bibliographic record
Abstract
Chapter 10 returns to broader issues of the cultural politics of metaphor, examining the tensions between ethics and aesthetics in illness experience and healing. While the focus on language allows us to mobilize the richness of literature to explore illness experience, in doing so we may inadvertently downplay the material circumstances that determine health disparities and inequities. Against this apparent opposition, I argue that attention to the aesthetics of language and the creative functions of imagination and poeisis can help us understand the mechanisms of suffering and affliction and devise forms of healing that better respond to the needs of individuals within and across diverse cultures and contexts. Every choice of metaphor draws from and points toward a form of life. The critique of metaphors that begins with an appreciation of the qualities they confer on experience, and then moves out into the social world to identify ways that systems and structures are configured, rationalized, and maintained. A critical poetics of illness and healing can contribute to efforts to improve our institutions and achieve greater equity not only by recognizing and respecting difference and diversity but also by engaging with the particulars of each person’s experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".