Bibliographic record
Abstract
Literature has had a long relationship with medicine through literary images of disease, literary images of physicians and other healers, works of literature by physician-writers, and the use of literature as a method of active or passive healing. Literature also has had a long relationship with the law through literary images of various legal processes, lawyers, and judges, works for literature by lawyer-writers, and the use of literature as therapy. At last count, eighty-four law schools in the United States and Canada reported offering some variations of a “law and literature” course and recent scholarship demonstrates that literature increasingly is being used to illuminate specific, and notoriously difficult, areas of the law such as tax law. Although more than a dozen U.S. law schools have established health law institutes, programs, centers, departments, or certifications, each of which offers a variety of health law courses ranging from “Alternative Medicine and the Law” to “Toxic Tort Litigation,” literature has yet to be routinely incorporated into these health law curricula. How can the field of law and literature inform the study of health law? And how can the field of literature and medicine help the field of law and literature in this regard?
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".