Bibliographic record
Abstract
Student-written evaluation (N = 600) of professors was examined to determine the emotional tone of the words used to evaluate faculty. Using the revised Dictionary of Affect (DOA; Whissell, 2009), evaluation words (N = 26,764) uploaded to the Rate My Professors website between 2018 and October of 2023 were measured for their pleasantness, activation, and imagery. Overall, the emotional tone of the students’ written evaluation was very close to the DOA’s definition of everyday English (M = 50) for all three categories: pleasantness (M = 51.1, SD = 6.3), activation (M = 52.2, SD = 4.8), and imagery (M = 50.2, SD = 7.4). The results indicated that the written evaluations were uniform in expression and emotional tone: neither very pleasant/unpleasant, active/passive, or imagery/abstract. While significant relationships were found with professor quality and difficulty ratings, the number of words in the evaluation, and the instructor’s gender, all associations had small correlational strengths and weak effect sizes, indicating that the variables might not be strong predictors of the emotional tone of student evaluations. If student written evaluations are not emotionally charged, then there is an opportunity to reduce any negative feelings faculty members have attached to the process.
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 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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".