Applicant Expectations and Decision Factors for Jobs and Careers in Food-Supply Veterinary Medicine
Bibliographic record
Abstract
This article examines the job expectations of applicants as reported by recruiters interviewing food-supply veterinary medicine (FSVM) candidates and the career-choice decision factors used by year 3 and 4 veterinary students pursuing careers in FSVM. The responses of 1,047 veterinary recruiters and 270 year 3 and 4 students with a food-supply focus from 32 colleges of veterinary medicine in the United States and Canada were examined. Recruiters were asked to report the two most important job factors applicants took into account when deciding to accept an offer; students were asked the two most important reasons for choosing a career in FSVM and the two most important benefits of working as a food-supply veterinarian. Recruiters reported that high salaries and good benefits are the two most important decision factors. Interest in the food-animal career area and a desire for a rural, outdoor lifestyle were the top reasons students gave for choosing an FSVM career. Students saw the enjoyment of working with and helping producers and food animals as the most important benefits of a career in FSVM.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".