Bibliographic record
Abstract
The Recognition Lecture is an annual honor awarded by the Association of American Veterinary Colleges (AAVMC) to an individual whose leadership and vision have made significant contributions to academic veterinary medicine and the veterinary profession. In 2012, this prestigious honor was bestowed upon Dr. Malcolm Getz, who has advanced the understanding of the economics of academic veterinary medical higher education and the private practice veterinary profession. Dr. Getz is an associate professor of economics in the department of economics at Vanderbilt University. He has been a faculty member in economics at Vanderbilt since 1973. He was Director of the Jean and Alexander Heard Library (1984–1994) and Associate Provost for Information Services and Technology (1985–1994). An accomplished writer, he has authored four monographs, two textbooks, and many essays. He earned his BA in economics from Williams College in 1967 and his PhD in economics from Yale University in 1973. In his lecture to attendees of the AAVMC Annual Meeting in March of 2012, Dr. Getz discussed the relationship between education and earnings across health professions. Dr. Getz has reviewed the economics of several different health science professions including medicine, dentistry, nursing, and physician assistants, as well as veterinary medicine. His breadth of understanding the strengths, weaknesses, and trends of these different professions provides insights into the future and subsequent modifications that will take place to sustain the benefits each profession offers to society at a reasonable cost. All health professions are facing challenges, economic and otherwise. By reviewing these challenges, the veterinary profession can avoid circumstances that are not economically viable for society to bear. Dr. Getz makes intriguing arguments for modification of educational programs, resulting in lower educational costs while still meeting the needs of society. —Bennie Osburn, DVM, PhD, DACVP, DAAVI, DAVES (hon.), Former Interim Executive Director, AAVMC; Dean Emeritus, School of Veterinary Medicine, University of California, Davis
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".