Taking on the Invisible Third Shift: The Unequal Division of Cognitive Labor and Women’s Work Outcomes
Bibliographic record
Abstract
The current research focuses on the gendered work-related impacts of the division of unpaid labor. Drawing upon the literature on gender roles and conservation of resources theory, we argue that women (vs. men) are particularly drained due to undertaking a greater proportion of cognitive labor—a hidden form of unpaid labor involved in managing a household—leading to undermined work outcomes. Data were collected weekly (during the early phases of the COVID-19 pandemic) for 7 weeks in April to May 2020 ( N = 263) and aggregated. Using multilevel structural equation modeling, we found that women (vs. men) reported engaging in a disproportionate amount of cognitive labor in their households, which increased their emotional exhaustion and, in turn, was related to greater turnover intentions and lower career resilience. However, for mothers (vs. fathers), emotional exhaustion and undermined work-related outcomes were driven by disproportionate responsibility for childcare. Hence, division of cognitive labor uniquely affected work-related outcomes of women without children, whereas division of childcare shaped the work-related outcomes of women with children. Overall, this research highlights the differential challenges faced by working women with and without children and the need for gender equity initiatives focusing on both women with and without children.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".