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Record W4417366328 · doi:10.5040/9781350506367

Art and the Critical Medical Humanities

2025· book· en· W4417366328 on OpenAlexaboutno aff
Fiona Johnstone, Allison Morehead, Imogen Wiltshire

Bibliographic record

VenueBloomsbury Publishing Plc eBooks · 2025
Typebook
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical humanitiesDigital humanitiesSpace (punctuation)LandmarkHuman sexualityWork (physics)Variety (cybernetics)

Abstract

fetched live from OpenAlex

This agenda-setting edited volume makes a forceful case for the contribution that art – its practices and its histories – can make to debates and developments in critical medical humanities today. Whilst medical humanities previously emphasised an instrumental attitude towards art and art-making, recent work has opened up a dynamic space in which art can critically and imaginatively operate. With urgent attention paid to constructions of race, gender, class, sexuality and disability, the artists, art historians, and scholars in related fields represented within this volume address new and pressing questions about structures and experiences of health, medical knowledge, care, therapy, and clinical research and education. With more than 40 contributors from a range of countries including the UK, Canada, the United States, Australia, Norway, Spain, and Germany, this landmark and multi-format collection addresses artworks from the sixteenth century to the present day, serving as a key reference point for researchers, practitioners, and educators working in medical humanities and art-aligned fields alike. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by the Social Sciences and Humanities Research Council of Canada and the Wellcome Trust.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0090.033
Scholarly communication0.0170.008
Open science0.0010.006
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0100.002

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 head0.025
GPT teacher head0.290
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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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