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Record W2935792156 · doi:10.3138/cbmh.246-012018

Medical History as Fine Art in American Mural Painting of the 1930s

2019· article· en· W2935792156 on OpenAlexvenueno aff
Bert Hansen

Bibliographic record

VenueCanadian Journal of Health History · 2019
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMuralPortraitPaintingAsideArtArt historySubject (documents)The artsFine artModern artVisual art of the United StatesHistory of medicineVisual artsModern medicineHistory of artHistoryPerformance artMedicineArchitectureClassicsLiteratureTraditional medicine

Abstract

fetched live from OpenAlex

To illuminate popular notions of medical progress during the inter-war era, this article examines four large mural projects depicting medical history. Aside from portraits of individual medical heroes, such as Pasteur and Lister, artists also created imagery strongly contrasting traditional and modern medicine in general. This analysis features the works of four stylistically distinct artists (Bernard Zakheim, Charles Alston, William C. Palmer, and Victor Arnautoff), whose 1930s murals may be viewed today in San Francisco, New York City (Harlem and Queens), and Palo Alto, California. These murals are significant works of art in themselves, and they form an unusual group of special interest to historians because they took on an uncommon subject for the fine arts - the history of medicine - rather than what had long been the far more common portrayal of medicine by artists, namely contemporary medical scenes from their own era.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.011
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

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