More Time with the Family? Workplace Flexibility Policies and Fathers’ Time with Children
Bibliographic record
Abstract
Workplace flexibility policies, which provide control over the timing and location of work, are a family-friendly resource that may facilitate increased father-child time. Yet, research on this relationship often focuses narrowly on “childcare time,” which not only overlooks the majority of time fathers spend with their children, but also neglects fathers’ accessibility to and responsibility for children as well as the co-presence of the mother. This limits our understanding of how flexibility policies might enhance family well-being and mitigate persistent gender inequalities. Using the 2017-2018 American Time Use Survey Leave Module, this study examines the relationship between flextime (control over start and end times) and flexplace (working from home) policies and different-sex partnered fathers’ time with children. Access to and use of flexibility policies are associated with more family time with children – when the mother is also present. This includes not only time in childcare but also other activities such as meals as well as supervision or accessibility. However, there are no differences in fathers’ solo parenting time. Ultimately, these findings elucidate a more comprehensive picture of how flexibility policies might shape father involvement, and complicate our understanding of the consequences of flexibility for family well-being and gender inequality.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".