Factors Affecting Track Selection by Veterinary Professional Students Admitted to the School of Veterinary Medicine at the University of California, Davis
Bibliographic record
Abstract
Factors affecting track selection before admission to the School of Veterinary Medicine at the University of California, Davis, and factors affecting change of tracks after the first two years of the curriculum were investigated by means of a survey of the 118 students of the graduating class of 2009. The student's background experience before admission to the School of Veterinary Medicine and other personal reasons were significant factors affecting small-animal and mixed-animal track choices. The student's background experience before admission to the School of Veterinary Medicine was the only significant factor for choosing the zoological track. The most significant factor for students to change their track from the mixed or zoological track to the small-animal track was background experience before admission to the School of Veterinary Medicine. Anticipated increased employment opportunities after graduation was the most significant factor for students to change their track from the mixed- or small-animal track to the zoological track. Other personal reasons was the significant variable for students to change their track from small-animal or zoological to mixed-animal track. Thus, to increase the number of students interested in tracks with lower enrollment, exposure of potential applicants to experience relevant to that track before admission and Admissions Committee selection criteria are likely to increase the odds of students' choosing that track.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".