Bibliographic record
Abstract
I was not surprised by Dr. Forrester's receiving the National Carl Norden–Pfizer Distinguished Teacher Award. At that time she had already been honored with an astonishing 17 college teaching awards, together with Virginia Tech's prestigious William E. Wine Award for Excellence in Teaching and induction into the university's exclusive Academy of Teaching Excellence. This is the kind of accomplishment to which many of us aspire but which precious few will achieve. The essence of Dr. Forrester's success is the notion that the student always comes first. She puts herself in the student's place, never forgetting what it is like to be subjected to the overwhelming burden of the traditional culture of detailed, didactic “coverage.” Dr. Forrester is a staunch advocate of an active/interactive, case-based clinical experience in which major concepts are more important than minutiae and technology is a catalyst for the learning process. Continual quality improvement is her motto. Dr. Forrester is an inspiring model of all that is good in veterinary medical education today. And the opportunity to write a prefatory note such as this is one of the joys of being an academic administrator. –Peter Eyre, Former Dean (1985–2003), Virginia–Maryland Regional College of Veterinary Medicine
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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 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".