Discussing <i>The Anatomy Table</i> and <i>The Vaccination Picture</i>
Bibliographic record
Abstract
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".