Making bodies, making kin: Storytelling and the professionalization of medical illustrators in North America
Bibliographic record
Abstract
Contemporary concerns about diversity and inclusion in medical practice demand a more nuanced understanding of medical illustrations as part of a larger system of medical knowledge informed by historical and economic conditions in which they are produced. This dissertation explores the professionalization, pedagogy, and practices of medical illustrators in North America since the First World War. I analyse medical illustrators’ professional formation and epistemic culture through a combination of archival research, interviews, and participant observation in graduate programs and professional gatherings, paying close attention to the role of gender in disciplinary formation. Graduate education transforms students from epistemic misfits into “storytellers” capable of bridging cultural binaries of art and science by reasserting colonial hierarchies of knowledge. In contrast to the patriarchal “founding father” narrative of professional emergence, the structural work of professionalization such as standardizing training and organizing professional bodies was carried out largely by female illustrators. Emphasis on metaphors of “family” and “storytelling” has enabled a feminized group of scientific workers to navigate an uncertain social and economic position by situating their knowledge practices within established institutions and forms of authority. However, positioning medical illustrators as subservient and limiting their knowledge claims ensures their continued invisibility as expert knowledge workers and limits their ability to challenge colonial conventions of representation. Exploring the making of medical illustrators presents an opportunity to reimagine their role in making medical knowledge.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".