Bibliographic record
Abstract
Edited by Allison Crawford, Rex Kay, Allan Peterkin, et al . Published by University of Toronto Press, 2012, paperback, 336 pages. ISBN 9781442612907, £22.95. Sometimes, I ask medical students to use a consultation from a different perspective to gain something other than the overt clinical presentation, and then to write a story or a poem (or a piece of music, a song, visual art) based on, and stimulated by, their observations. This allows them to concentrate on other aspects of the transaction without having to exercise their clinical acumen. It is a chance to observe more particularly the non-verbal language of both the patient and clinician, to note their interactions, the position of the chairs, the dynamics of the room, and the impact of another person, partner, parent, friend and child. What might the student gain from this approach? Additional information from the patient, certainly. Perhaps more insight into the effects of the circumstances on the patient, the physician and the companion. Perhaps an increase in self-awareness. These are all aspects that experienced clinicians will work into their consultations. The more so by giving patients time to tell their stories. But the …
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".