The effects of perceived professor competence, warmth and gender on students’ likelihood to register for a course
Bibliographic record
Abstract
The present research examined whether students’ likelihood to take a course with a male or female professor was affected by different expectations of professors based on gender stereotypes. In an experimental vignette study, 503 undergraduate students from a Canadian university were randomly assigned to read a fictitious online review, similar to those found on RateMyProfessors.com, that varied professor gender, overall quality score and level of caring for students. Students responded to items assessing their likelihood to take a course with the professor, perceived competence and warmth of the professor, and their own gender bias. An analysis of variance revealed an interaction between professor gender, student gender, quality score and caring. When quality score was low, male students indicated a lower likelihood of taking a course with female professors who were not described as caring. Regression analyses showed, however, that students' gender bias was negatively associated with likelihood to take a course with a female professor. These results imply that student gender plays a role in evaluations of female professors who do not display stereotypical warmth but that gender bias, which is typically higher for males at the group-level, may be an underlying factor.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".