Admissions Procedures at the University of Veterinary Medicine Vienna, Austria
Bibliographic record
Abstract
RATIONALE FOR THIS STUDY: The admission procedure implemented in fall 2005 in consequence of new laws passed in summer 2005 is described and evaluated. The general set-up, the underlying considerations, and the changes resulting from the establishment of this procedure are presented. METHODOLOGY: Admission variables and demographic information (sex, age, nationality) for 172 students who entered their first academic year at the University of Veterinary Medicine Vienna (VUW) and their academic performance measured according to their results in the three first-year examinations (successful versus unsuccessful) were assessed. Logistic regression was used to examine the relationship between predictor and outcome. RESULTS: Regression analysis indicates that Austrian students were more likely to be unsuccessful than German students (R(2) = 0.366, p < 0.001). Previous school performance was the best predictor for success in the Austrian cohort (R(2) = 0.196; p < 0.001), whereas the personal interview scores provided the best predictor in the German cohort (R(2) = 0.122; p < 0.05). CONCLUSION: This study supports our existing selection practices relating to cognitive and non-cognitive skills. The number of strugglers may be reduced on the basis of an admissions procedure, but struggling may not be excluded by the existing selection practices, which do not assess a specific threshold but are related to a score-ranking system and a predefined number of available openings in the program.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".