Bibliographic record
Abstract
This paper uses Rate My Professors (RMP) data for instructors at two Canadian universities to investigate the determinants of economics instructor ratings, including the impact of easy grading expectations (or “easy expectations”) and various potential student biases related to ethnicity, gender, and accent. Regression analysis, including random effects panel data analysis and multilevel modelling, indicates that easier courses with lower difficulty levels and higher grades awarded to students are significant determinants of better instructor ratings. In addition, lower difficulty levels and higher grades tend to be associated with contract instructors compared to full-time instructors. The effect of instructor accent was insignificant. Our findings suggest that the ratings of economics instructors suffer from the same biases related to course difficulty, possibly attributable to “easy expectations,” and racial bias, as has been generally found in student ratings across academic disciplines. To the extent that instructor ratings are driven by “easy expectations” and racial bias, and that RMP ratings are consistent with formal university instructor ratings, the case for basing promotions, tenure decisions, and salary raises on average instructor ratings is weak.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".