The Experience of Using CASPer at One College of Veterinary Medicine in the United States
Bibliographic record
Abstract
CASPer (Computer-based Assessment for Sampling Personal characteristics), an online, open-response situational judgment test, screens applicants to educational programs early in the selection process for non-cognitive abilities. CASPer was applied in Purdue University's DVM admissions cycle in 2020–2021 in two ways: applicants met a threshold criterion of eligibility for further review by having a reported CASPer Z-score >–1.5; and applicants were awarded points for Z-score >0 during in-depth review. The requirement that applicants meet a threshold CASPer Z-score affected male, underrepresented minority (URM) and international students adversely: higher proportions of these individuals were eliminated early from admissions consideration. Among those with Z-score >–1.5, first-generation, male, URM, and international applicants scored lower, on average, than non–first-generation, female, and non-URM applicants. Additionally, Z-scores were correlated with undergraduate grade point average (uGPA). The correlation coefficient r of Z-scores with cumulative and core uGPA was 0.20 ( p < .001) and 0.12 ( p < .001), respectively, in all applicants. Hoping to select high-potential applicants without overemphasizing cognitive abilities, we expected CASPer Z-score to be independent from uGPA which is already weighted appropriately in our process. The nonrandom distribution of Z-scores systematically influenced early rejection and possibly biased later selection of applicants. Concerned that CASPer incorporation may have unintentionally narrowed access to DVM admissions for some applicant groups, and not seeing added value in a standardized test whose predictive validity remains understudied in veterinary medicine—a profession aiming to diversify its workforce—our admissions committee suspended CASPer as an admissions criterion until systematic, multi-source information becomes available within the profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".