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Record W3082903479 · doi:10.17742/image.in.11.2.4

Discussing <i>The Anatomy Table</i> and <i>The Vaccination Picture</i>

2020· article· en· W3082903479 on OpenAlexvenueaboutno aff
Sean Caulfield, Timothy Caulfield, Johan Holst

Bibliographic record

VenueImaginations Journal of Cross-Cultural Image Studies · 2020
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsTable (database)VaccinationAnatomyMedicineComputer scienceVirologyData mining

Abstract

fetched live from OpenAlex

The Anatomy Table is a print-based work that thematically addresses the loss of public trust in science, as well as misinformation surrounding science-informed interventions in health care, such as vaccination. Drawing on the history of anatomical illustration by referencing Andrea Vesalius’s famous 16th-century anatomical book, On the Fabric of the Human Body, the work combines this with contemporary drawings that suggest anatomy but which have an imagined, nonsensical quality, indicating to viewers that the drawings are not accurate representations of human anatomy. In addition to reflecting on this piece and the process of collaboration, Caulfield, Caulfield, and Holst discuss the challenge of countering misinformation in healthcare today. The work was created through collaborative dialogue between Sean Caulfield, a professor in the Department of Art and Design at the University of Alberta, Timothy Caulfield, Canada Research Chair in Health Law and Policy at the University of Alberta, and Johan Holst, senior scientist previously working at the Norwegian Institute of Public Health in Oslo and from August 2016 being a vaccine expert at the Headquarter of CEPI (Coalition for Epidemic Preparedness Innovations), situated in Oslo, Norway.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0010.005
Open science0.0000.000
Research integrity0.0000.001
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 head0.372
Teacher spread0.346 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes2
Has abstractyes

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