The reincarnation of a biomedical researcher: from bench science to medical education
Bibliographic record
Abstract
After 33 years as a biomedical research scientist, I embarked on a new career in medical education. The transformation was awkward, difficult and exciting. Although I had assumed that previous experience in research and scholarship would stand me in good stead, such was hardly the case. I had to learn to navigate a strange new literature, replete with terms that I did not understand, and to deal with concepts that challenged my physico-chemical mindset. As I learned, I found myself discovering a field rich in essential questions, controversial hypotheses, and important potential applications. With my newly acquired knowledge and skills, I began to reflect on my own educational endeavors. I identified a number of outstanding issues and I designed studies to address them. What made these investigations particularly significant for me was their applicability. Although medical education is an exciting and meaningful career path, because of its low profile in most medical schools, few faculty are aware of the academic opportunities that it affords.
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 | 0.048 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".