Teaching Literature and Medicine
Bibliographic record
Abstract
Both the actualities and the metaphorical possibilities of illness and medicine abound in literature: from the presence of tuberculosis in Franz Kafka's fiction or childbed fever in Mary Shelley's Frankenstein to disease in Thomas Mann's Death in Venice or in Harold Pinter's A Kind of Alaska; from the stories of Anton Chekhov and of William Carlos Williams, both doctors, to the poetry of nurses derived from their contrasting experiences. These are just a few examples of the cross-pollination between literature and medicine.It is no surprise, then, that courses in literature and medicine flourish in undergraduate curricula, medical schools, and continuing-education programs throughout the United States and Canada.This volume, in the MLA series Options for Teaching, presents a variety of approaches to the subject. It is intended both for literary scholars and for physicians who teach literature and medicine or who are interested in enriching their courses in either discipline by introducing interdisciplinary dimensions.The thirty-four essays in Teaching Literature and Medicine describe model courses; deal with specific texts, authors, and genres; list readings widely taught in literature and medicine courses; discuss the value of texts in both medical education and the practice of medicine; and provide bibliographic resources, including works in the history of medicine from classical antiquity.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".