Bibliographic record
Abstract
The hematologist author was consulted as an expert on a case leukemia, by reading a set of bone marrow samples “blind” without clinical information. The patient had clearly been treated with chemotherapy, but the doctor was later surprised to learn that she considered herself healed through the intercession of a woman who had been dead for 200 years. This cure was deemed miraculous, and on its strength, Marguerite d’Youville became the first Canadian-born saint. Now more sensitized to saints, Duffin soon noticed that the ancient twin physicians, Saints Cosmas and Damian, were enjoying a robust revival in Canada and the United States. She began a search to find out why. The work led her to sociological, genealogical, and psychological theories, and to libraries, archives, great cities and obscure villages across North America and Europe. She also conducted surveys with pilgrims at feast-day celebrations. The investigation eventually produced some surprising connections with medical greats and with twins healers from other religions. But, for her, the biggest discovery was a new perspective on medicine and its parallel functions with religion. A scholarly work written autobiographically, Medical Saints is not only the history of the veneration of Cosmas and Damian, and other healing saints; it is a history of the research project itself.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.023 |
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".