K1S 5B6The effect of hotness on pay and productivity
Bibliographic record
Abstract
Acknowledgements: We would like to thank Hideki Ariizumi and Natasha De Sousa for their contribution to data gathering, Martin Dooley and Elaine Constant for helping us track down rank data, and Kim Lehrer for excellent comments at the CEA meetings in Quebec City. 1 Abstract: In this paper we examine the impact of a professor’s hotness, as rated by students, on his or her salary, controlling for research and teaching productivity. We also estimate the impacts of a professor’s hotness on the quality of his or her teaching, as evaluated by students, and the impact of hotness on research productivity, as measured by citations, publications, co-authorship, and grant funding. Our study is based on data describing economics professors at sixteen universities. Although a relatively small proportion of our sample is rated “hot ” by students, hotness generates, for some, a significant earnings premium, even with comprehensive controls for productivity. We find a strong relationship between hotness and teaching productivity, but a much weaker relationship between hotness and research productivity. The unique contribution of this paper is the use of data on actual productivity, which is generally unavailable in papers assessing the returns to appearance. 2 The effect of hotness on pay and productivity Our paper is motivated, first, by a long-standing puzzle: Are beautiful people paid more because
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.183 | 0.023 |
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".