Students Aggress Against Professors in Reaction to Receiving Poor Grades: An Effect Moderated by Student Narcissism and Self‐Esteem
Bibliographic record
Abstract
Laboratory evidence about whether students' evaluations of teaching (SETs) are valid is lacking. Results from three (3) independent studies strongly confirm that "professors" who were generous with their grades were rewarded for their favor with higher SETs, while professors who were frugal were punished with lower SETs (Study 1, d = 1.51; Study 2, d = 1.59; Study 3, partial η(2) = .26). This result was found even when the feedback was manipulated to be more or less insulting (Study 3). Consistent with laboratory findings on direct aggression, results also indicated that, when participants were given a poorer feedback, higher self-esteem (Study 1 and Study 2) and higher narcissism (Study 1) were associated with them giving lower (more aggressive) evaluations of the "professor." Moreover, consistent with findings on self-serving biases, participants higher in self-esteem who were in the positive grade/feedback condition exhibited a self-enhancing bias by giving their "professor" higher evaluations (Study 1 and Study 2). The aforementioned relationships were not moderated by the professor's sex or rank (teaching assistant vs.professor). Results provide evidence that (1) students do aggress against professors through poor teaching evaluations, (2) threatened egotism among individuals with high self-esteem is associated with more aggression, especially when coupled with high narcissism, and (3) self-enhancing biases are robust among those with high self-esteem.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".