Bibliographic record
Abstract
The practice of medicine is an art, not a trade; a calling, not a business; a calling in which your heart will be exercised equally with your head. This book is about education. While the subject is about teaching and learning professionalism, the authors discuss how best to educate the physicians of the future who will be responsible for much of the health and well-being of their fellow citizens. While medical education often appears to have developed in isolation from the formal world of pedagogy, medical students are adult learners and the science of cognition applies to them as it does to other learners. Through the centuries, we have come to understand a great deal about education, but there is still much that we do not know and probably will never fully comprehend. For a period of time, both general and medical education placed great emphasis on the acquisition of knowledge and skills. In our knowledge-based world, this is certainly appropriate, as one cannot function without a minimal level of knowledge. However, recent times have seen a return to an earlier belief that education represents more than facts and figures. It has been said that education is what remains after what has been learned has been forgotten. Michael Polanyi, that wonderful combination of chemist and philosopher, coined the term “tacit knowledge” to help us understand this phenomenon. He stated that “one knows things which one cannot tell.” Tacit knowledge is acquired through experiencing a broad spectrum of life's challenges.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.380 | 0.194 |
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".