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Record W6959061666 · doi:10.1017/9781009617802.010

Epilogue: Ethics, Aesthetics, and Poiesis

2025· book-chapter· en· W6959061666 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2025
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoeticsMetaphorPoliticsDiversity (politics)Equity (law)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.961
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.187
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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