Bibliographic record
Abstract
New York University's Literature, Arts and Medicine Database (endeavor. med .nyu .edu/lit-med/ lit-med-db/ top view .html) began as a resource for medical educators, but today it also serves any reader in search of a good book. Its 41st edition features annotations for 53 artworks and 90 films, as well as 1461 literary works by 894 authors. A free-text search for “Canada” finds 63 entries, including books by authors such as Jane Urquhart, Timothy Findley, Hugh MacLennan and Janet Turner Hospital, and films such as Strangers in Good Company. The individual entry for Toronto author Donna McFarlane's 1994 Division of Surgery provides a brief summary and commentary about the work, as well as publication information and keywords. The commentaries are informative and accessible, and the indexing by keywords is one of the site's most useful features. Division of Surgery, a novel about a young woman surviving serious inflammatory bowel disease, is indexed under keywords such as doctor–patient relationships, human worth, patient experience and power relationships. Clicking on any one of these terms pulls up a list of related entries, allowing the user to search readily for works dealing with a particular theme. Certain entries are cross-linked to online texts or audio or visual presentations where the works are published, displayed, excerpted, read, performed or discussed. Links from the Medical Humanities homepage (http://endeavor.med.nyu.edu/lit-med) lead to the Web site for the journal Literature and Medicine (http://muse.jhu.edu/journals/literature_and_medicine) and the Roster of Physician Writers (http://members.aol.com/dbryantmd) . Newcastle University's Walton Library encourages students to broaden their reading by visiting www.ncl.ac.uk/library/medical/medhums.html, which is linked to, among other resources, its own compilation of literature about medicine. —
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.858 | 0.860 |
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".