Bibliographic record
Abstract
How clinicians learn in practice has to be as important a question in veterinary medicine as it is in human medicine. And while I have no information bearing on how veterinarians learn, I have been studying physician learning long enough to offer some thoughts about how they learn—and I will do that in the course of addressing the first of the two goals set for this paper. I can address the first goal as the paper’s author, while the second requires your participation as a reader. The first goal is to describe how doctors learn in clinical practice. The second goal is to decide how well the theory presented also describes veterinarians individually and as a profession. It’s my anticipation that having read this paper and having considered how it resonates with your and your colleagues’ practices, you will have a broadened perspective on learning in veterinary clinical practice.
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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".