Forecasting Veterinary School Admission Probabilities for Undergraduate Student Profiles
Bibliographic record
Abstract
Increased competition for veterinary school admission has created a need to determine whether individual students are likely to be successful candidates for veterinary school admission early in their undergraduate careers. Students invest considerable time and money in pre-veterinary courses of study, hoping for acceptance into professional veterinary school. A forecasting model was developed to predict the likelihood of students with particular characteristics gaining acceptance. Characteristics such as gender, age, size of high school, and ACT, are known upon entrance into college and can be used to determine the likelihood of an individual's being accepted. Data were gathered from the Louisiana State University College of Veterinary Medicine (LSU-CVM) admissions for all students applying to veterinary school for the classes of 2006 through 2008 from the top two agricultural programs in the state in terms of quantity of applicants to veterinary school: Louisiana State University and Louisiana Tech University. A one-way ANOVA was used to examine whether there were any statistical differences between known demographic and performance variables and acceptance into veterinary school. A logit forecasting model was then estimated to predict the likelihood of gaining acceptance into veterinary school based only on variables known early in the student's undergraduate career. Age, gender, and ACT scores were determined to be important variables in determining the likelihood of gaining admission. Overall, the forecasting model is of use in assigning probabilities of acceptance into veterinary school for specific student profiles, which can assist in one-on-one assistance from advisor to student.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".