Taking the Time: The Implications of Workplace Assessment for Organizational Gender Inequality
Bibliographic record
Abstract
Gendered differences in workload distribution, in particular who spends time on low-promotability workplace tasks—tasks that are essential for organizations yet do not typically lead to promotions—contribute to persistent gender inequalities in workplaces. We examined how gender is implicated in the content, quality, and consequences of one low-promotability workplace task: assessment. By analyzing real-world behavioral data that include 33,456 in-the-moment numerical and textual evaluations of 359 resident physicians (subordinates) by 285 attending physicians (superordinates) in eight U.S. hospitals, and by combining qualitative methods and machine learning, we found that, compared to men, women attendings wrote more words in their comments to residents, used more job-related terms, and were more likely to provide helpful feedback, particularly when residents were struggling. Additionally, we found women residents were less likely to receive substantive evaluations, regardless of attending gender. Our findings suggest that workplace assessment is gendered in three ways: women (superordinates) spend more time on this low-promotability task, they are more cognitively engaged with assessment, and women (subordinates) are less likely to fully benefit from quality assessment. We conclude that workplaces would benefit from addressing pervasive inequalities hidden within workplace assessment, equalizing not only who provides this assessment work, but who does it well and equitably.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".