Grade Inflation at a North American College of Veterinary Medicine: 1985–2006
Bibliographic record
Abstract
Grade inflation, an upward shift in student grade-point averages without a similar rise in achievement, is considered pervasive by most experts in post-secondary education in the United States. Grade-point averages (GPAs) at US universities have increased by roughly 0.15 points per decade since the 1960s, with a 0.6-point increase since 1967. In medical education, grade inflation has been documented and is particularly evident in the clinical setting. The purpose of this study was to evaluate grade inflation over a 22-year period in a college of veterinary medicine. Academic records from 2,060 students who graduated from the College of Veterinary Medicine at Kansas State University between 1985 and 2006 were evaluated, including cumulative GPAs earned during pre-clinical professional coursework, during clinical rotations, and at graduation. Grade inflation was documented at a rate of approximately 0.2 points per decade at this college of veterinary medicine. The difference in mean final GPA between the minimum (1986) and maximum (2003) years of graduation was 0.47 points. Grade inflation was similar for didactic coursework (years 1-3) and clinical rotations (final year). Demographic shifts, student qualifications, and tuition do not appear to have contributed to grade inflation over time. A change in academic standards and student evaluation of teaching may have contributed to relaxed grading standards, and technology in the classroom may have led to higher (earned) grades as a result of improved student learning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".