Bibliographic record
Abstract
[First para.]: " When the editors of UTMJ introduced the journal’s new “Philosophy and Medicine” section in December 2003, they explained that its purpose was “to provide a forum for students to explore the interface between diverse schools of thought and how they contribute to the practice of modern medicine”. This move – providing space in the journal for health researchers from various backgrounds to share their knowledge and experience – reflects a shift in the landscape of health and medicine. As neurologist and professor of Medical Humanities T.J. Murray explained in 1998, “We often use the term ‘medical science’ but this refers to the scientific knowledge used by medicine. Medicine is not a science. It is a caring profession that uses science.”2 The shift to a more encompassing idea for health is consistent with changes to the health research agenda in Canada. The Canadian Institutes of Health Research (replacing the Medical Research Council) recognises that advances in biomedicine are a key factor, but not the only factor, in improving the overall health of Canadians. The social sciences and humanities can suggest ways of tracking some of the psychological and emotional – as well as socio-economic, cultural, ethical, and interpersonal – elements of health and health care."
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.088 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".