The Italian Veterinary Medicine Admission Test: Analysis of Student Intake in the Years 2007, 2008, and 2009, and of the Test's Relationship with Students' Academic Careers
Bibliographic record
Abstract
The present paper analyzes the admission test administered to candidates to the veterinary medicine program in Italy for the academic years 2007-2008, 2008-2009, and 2009-2010 nationwide as well as the University of Pisa student intake from 2001-2002 through 2009-2010, comparing the relationship between the admission test and students' academic careers at Pisa. This paper finds that the Italian system of a locally enforced fixed intake number does not select the best possible candidates for admission because (1) there are significant variations in the candidates' preparation among the different locations where the test is held, (2) the subjects tested are not equally selective in identifying the best candidates, and (3) there is a very strong relationship between candidates' performance on the admission test and the subsequent academic career of the admitted candidates. In its findings, this study in part contradicts what is commonly believed by the Italian veterinary medicine community, and, as a result, it is extremely important that care is taken in the decision-making process-in the process, that is, of identifying a fixed intake number and of selecting the subjects to be tested on the admission test.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".