REAL COSTS OF NOMINAL GRADE INFLATION? NEW EVIDENCE FROM STUDENT COURSE EVALUATIONS
Bibliographic record
Abstract
College grade point averages in the United States rose substantially between the 1960s and the 2000s. Over the same period, study time declined by almost a half. This paper uses a 12‐quarter panel of course evaluations from the University of California, San Diego to discern whether a link between grades and effort investment holds up in a micro setting. Results indicate that average study time would be about 50% lower in a class in which the average expected grade was an “A” than in the same course taught by the same instructor in which students expected a “C.” Simultaneity suggests estimates are biased toward 0. Findings do not appear to be driven primarily by the individual student's expected grade, but by the average expected grade of others in the class. Class‐specific characteristics that generate low expected grades appear to produce higher effort choices—evidence that nominal changes in grades may lead to real changes in effort investment. (JEL I21, J22, J24)
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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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".