Delia Gavrus and Susan Lamb, Transforming Medical Education: Historical Case Studies of Teaching, Learning, and Belonging in Medicine
Bibliographic record
Abstract
could be a weekday as well as a Sunday word.Diffusion of ideas in this roundabout manner is so expressive of English political instincts and cultural preferences that it deserves a much closer study.Crone's camera obscura does not contemplate a static landscape and while the larger ideas of church and state are given proper attention, she draws us through the doors of the local schools and has much to say about the range of educational experiments, the philanthropic bodies, the stubborn (and sometimes grumpy) reactionaries and the perennial fretters about life's illusions and traps.It is of course fitting that this engrossing study, telling us so much about institutional possibilities and walking us through such a gallery of individuals, should come from that remarkable seat of learning and improvement -the Open University.In the course of prison research and inspection visits over many years, inmates have occasionally told me of the hope that entered their lives through education.For those who could make the commitment, the Open University had a huge impact.Its pioneering remote study techniques, materials and assessment methods carried selfrespect and a sense of achievement over the walls; its range of subjects opened windows and doors.A fragile thread connects us to Crone's locked-up men and women, stumbling though spelling-books and times' tables.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".