More than employment policies? Parental leaves, flexible work and fathers’ participation in unpaid care work
Bibliographic record
Abstract
This article explores two policy pathways – parental leave and flexible work –as complementary policy interventions aimed at promoting gender equality in unpaid care and household work. Drawing on Canadian data from the 2021 International Familydemic Survey, we examine the relationship between fathers’ previous use of parental leave, and current use of flexible work arrangements (flextime and remote work), and their involvement in unpaid care work during the COVID-19 pandemic. Our findings support the following three arguments: First, in numerous countries, including Canada, where socially exclusive policy designs can limit fathers’ take up of parental leave, flexible work arrangements can provide additional opportunities to increase fathering involvement beyond the early months of parenting. Second, our data indicate that unpaid care work sharing is enhanced by fathers’ parental leaves and flexible working; however, fathers who have taken parental leave report dividing a wider set of household work and care tasks with their partners. Third, although their policy designs, aims, and legislation architectures differ in Canada, we maintain that parental leaves and flexible work arrangements are both more than employment policies; they are care/work policies that enact ‘social care’ and ‘democratic care’, and support gender equality and work-family justice goals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".