Skills, Knowledge, Aptitude, and Attitude Colloquium
Bibliographic record
Abstract
Three projects recently funded by the American Veterinary Medical Association (AVMA) through the National Commission on Veterinary Economic Issues (NCVEI) focused on the veterinary school applicant pool, leadership skills in the veterinary profession, and a veterinary teaching hospital business model, respectively. The Skills, Knowledge, Aptitude, and Attitude (SKAs) Colloquium was designed to present the results of these three projects, to discuss their importance for the future of the veterinary profession, and to develop action plans accordingly. In all, 24 veterinary colleges were represented at the workshop and a total of 72 attendees participated, achieving a broad representation of the veterinary profession ( both academic and non-academic). Through an orchestrated combination of general sessions and facilitated small group discussions, recommendations for implementation and initial action plans for next steps were developed. From these, a list of potential AAVMC follow-up activities was developed, including advocating and facilitating programs across schools to engage and educate faculty regarding the results of these projects; developing realistic information on careers in veterinary medicine; organizing an AAVMC leadership consortium; working toward further development and implementation of the veterinary teaching hospital (VTH) business model; coordinating and sponsoring a national forum on the future of the VTH; reviewing admissions processes; integrating leadership into veterinary curricula; and organizing opportunities for faculty development in leadership.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".