Reply to commentary to: Gender and rank salary trends among academic dermatologists
Bibliographic record
Abstract
We thank Lipner et al. for their interest in our article and appreciate their effort in highlighting the contribution of gender in salary disparities among Veterans Affairs (VA) dermatologists.In their recently published article, Do and Lipner (2020) described the discrepancies in compensation for male and female VA dermatologists.Multivariate analysis showed that, overall, gender was not a significant contributor to VA dermatologists' salaries, and instead h-index, academic rank, and years since graduation were significant contributors.Their study found that true gender-based salary disparity was only noted regionally, specifically in the Midwest.We concur that female dermatologists are underrepresented in higher academic ranks, and a significant salary gap remains prevalent despite a narrowing of the gap between 2013 and 2018.These trends suggest a need for further studies over a longer time period that incorporate the previously mentioned factors affecting salary to identify the extent of gender-based salary gaps.Our paper reports the results of a pilot study using the Faculty Salary Survey database.We discussed how the lack of consideration for faculty salary based on full-time equivalent, geographic influences, clinical versus nonclinical faculty, and academic tracks are limitations of this database.The challenge in performing the same analysis using the Association of American Medical Colleges faculty database is, unlike the VA data, the lack of access to complete individual demographic data.This precludes a comprehensive analysis of the confounding factors Lipner et al. mention, such as h-index and other academic merits.We also agree that measures are likely being implemented to address gender salary gaps, as the commenters mention a higher median salary growth rate in women versus men in some ranks.However, despite the trend towards closing this gap observed in the 5-year period, there remains an unequal distribution of higher academic ranks between men and women.Many academic faculty members have employment at multiple nearby institutions.For example, a faculty member can have appointments and salary sources at the university medical center, VA, or state children's hospital.Various factors may contribute to the existence and size of gender discrepancies within different institutions.A larger-scale study than ours and the commenters' that compares salary sources and additional confounding factors is needed.We also need increased transparency and more accuracy in reporting and accessing other incentives and outside sources of income to have a more complete picture of compensation discrepancies between male and female dermatologists.
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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 | 0.005 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.049 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".