Disparate Impact? Career Disruptions and COVID-19 Impact Statements in Tenure Evaluations
Bibliographic record
Abstract
Extensive research reveals employer biases against workers with career disruptions, particularly those related to caregiving. However, the effectiveness of organizational practices intended to mitigate such biases is less well understood. This study examines the use of COVID-19 impact statements in tenure decisions at research universities, an organizational intervention that was designed to reduce biases but raised concerns that it might inadvertently amplify them. Contrary to concerns about unintended consequences, a pre-registered survey experiment with 602 full professors in STEM fields reveals that the inclusion of impact statements leads to more favorable tenure evaluations, regardless of faculty gender and disruption type. Qualitative evidence suggests that perceptions of pandemic-related disruptions as legitimate, externally imposed, time-limited events in the past help circumvent previously documented biases. This study enhances our understanding of organizational practices that effectively mitigate biases and points to the potential role of narrative framing in workplace evaluations and organizational inequalities.
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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 | 0.032 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".