RateMyProfessors.com™: The Impact of Negative Online Professor Reviews on Student Judgement
Bibliographic record
Abstract
Negatively-valenced emotional expressions (NVEE) are identified by the use of extreme language, emoticons, bold lettering, capitalization, and exclamation marks. When used in online review forums, NVEE are indicative of the severity of negative reviews, which may be perceived as less valid than negative reviews without NVEE. We sought to examine the effects of NVEE on student likelihood to take a professor’s class. We presented 51 university students with reviews based on RateMyProfessors.comTM. Students were randomly assigned to one of three conditions: positive reviews, negative reviews with NVEE, or negative reviews without NVEE. We found that students who viewed the positive reviews were significantly more likely to take the course than those who viewed negative reviews. Contrary to our prediction, the negative reviews with NVEE condition did not indicate greater likelihood of taking the course to the negative review condition without NVEE. However, qualitative analysis of student response to reviews showed that students were skeptical of reviews with NVEE, indicating that this research is relevant and useful for understanding what makes online reviews helpful.
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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 | 0.007 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".