The Beauty Premium in the Academic World: A Cross-Cultural Perspective
Bibliographic record
Abstract
A majority of studies on the beauty premium of college professors have been conducted in Western countries, mainly in the United States and Canada. The present paper focuses on the influence of professors’ physical attractiveness on their teaching ratings awarded by three ethno-cultural student populations (native Israelis, FSU immigrants, and Ethiopian immigrants) at a large public college in Israel. We asked the participants to look at photographs of attractive professors (rated in a previous study) and rate the quality of their teaching, based solely on the photographs. It was found that both female and male professors were awarded a beauty premium by all three groups. Our findings confirm results of previous studies in USA and Germany, which suggested that the beauty premium exists across diverse ethnic groups. Our conclusions are based mainly on findings that were obtained from Christian or Jewish subjects. A more comprehensive examination could include additional cultures and religions, such as Moslem or Buddhist participants. Therefore, we would welcome collaboration with colleagues who are interested in examining this phenomenon in their own countries. Our conclusion may have practical implications when evaluating employees' performance in the globalized multicultural workforce in general, and in intercultural academic settings in particular.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".