Recovery from university grade inflation after the COVID-19 pandemic varies by faculty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recovery from university grade inflation after the COVID-19 pandemic varies by faculty Higher education institutions worldwide report a drastic increase in grades during the COVID-19 pandemic. Since it was not paralleled by an increase in student achievements, the consensus is that the pandemic came with grade inflation, which exceeded the systematic multiannual trends of grade inflation. The present paper adds two novel aspects to this body of research. First, unlike most existing reports, this study covers the post-pandemic timespan and quantifies the degree of return to pre-pandemic grading patterns. We examine changes in grade point average (GPA) values in undergraduate students from two Canadian universities (N = 32,753 and N = 15,101) in the circum-pandemic period from 2018–2019 through 2022–2023. Second, we consider changes in grading by university faculty. The central finding in both universities is that post-pandemic GPAs returned fully to pre-pandemic levels only in some faculties (e.g. Business, Engineering, Health Sciences) but are still inflated in others (e.g. Humanities, Social Sciences) compared to pre-pandemic levels. Thus, post-pandemic grades stemming from some faculties are more trustworthy, i.e. exhibit less inflation and are more closely aligned with student achievement, than in other faculties. We provide correlational evidence that the faculties that have curbed grade inflation more successfully are the more selective ones and arguably have more incentives to maintain higher educational standards.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it