Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it