Supporting women during motherhood and caregiving necessary, but not sufficient: The need for men to become equal partners in childcare
Bibliographic record
Abstract
The timely focal article by Gabriel et al. (2023) spotlighted critical issues that women academics face around fertility, pregnancy, motherhood, and caregiving.We add to this perspective by arguing that it is not sufficient to only focus on women during motherhood and caregiving, and that for women to truly succeed in academia (and beyond) we need men to become equal partners in childcare, alleviating the burden placed mostly on women.We thus aim to highlight that childcare is not only a women's issue but rather a parents' issue.Positioning childcare and caregiving as a women's issue risks perpetuating traditional gender stereotypes.Instead, bringing men into the picture provides both men and women with greater opportunities to succeed in both work and family domains. 1 We therefore argue that, in addition to supporting women during motherhood and caregiving, traditional gender stereotypes need to be challenged, and men should be encouraged and provided with pathways to become equal partners during these critical periods.This, we believe, can in turn help change the norms surrounding childcare and performance cultures in academia, which are not aligned with caregiving.Although we argue that solely focusing on mothers as caregivers is insufficient, we acknowledge that it is reasonable that the focal article focuses on women, given that it is based on experiences of women faculty in the United States where basic needs for mothers such as mandated paid parental leaves do not exist.In presenting our arguments below, we also note we are women academics working at Canadian universities, and thus we bring the Canadian perspective to this commentary.By doing so, we highlight that while the experiences described in the focal article are similar for women across the globe, there are also differences across countries.Given that our commentary is rooted in the Canadian context, which critically differs from the US in terms of policies, we first present a brief description of the Canadian context.The last three authors have made equal contributions and are listed alphabetically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".