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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".