Knowledge as Practice: Rethinking Medical Education
Bibliographic record
Abstract
There is a long tradition of general internists being involved in medical education as teachers, educational administrators, and, increasingly, medical education scholars and researchers. In recent years, the medical education journals have contained rather lively and impassioned debates about the best ways in which to carry out that sort of research. During the first years of the 21st century, editorial debates in the top journals in the field (e.g., Medical Education, Academic Medicine, Advances in Health Sciences Education) were divided on such basic issues as these: (1) What are the most important research questions that medical education research should be addressing?1 – 3(2) Who is the target audience of that research – the deans and course directors and clinical teachers, or other researchers in the field?3 , 4(3) Which research methodologies should be used?5 – 10These issues were also discussed at conferences, in meetings, and informally among colleagues.
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 | 0.132 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.010 | 0.172 |
| Scholarly communication | 0.036 | 0.053 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.016 | 0.025 |
| 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".