The unintended consequences of maternity leaves: How agency interventions mitigate the negative effects of longer legislated maternity leaves.
Bibliographic record
Abstract
To support women in the workplace, longer legislated maternity leaves have been encouraged in Scandinavian countries and recently in Canada. Yet, past research shows that longer legislated maternity leaves (i.e., 1 year and longer) may unintentionally harm women's career progress. To address this issue, we first sought to identify one potential mechanism underlying negative effects of longer legislated maternity leaves: others' lower perceptions of women's agency. Second, we utilize this knowledge to test interventions that boost others' perceptions of women's agency and thus mitigate negative effects of longer legislated maternity leaves. We test our hypotheses in three studies in the context of Canadian maternity leave policies. Specifically, in Study 1, we found that others' lower perceptions of women's agency mediated the negative effects of a longer legislated maternity leave, that is, 1 year (vs. shorter, i.e., 1 month maternity leave) on job commitment. In Study 2, we found that providing information about a woman's agency mitigates the unintended negative effects of a longer legislated maternity leave on job commitment and hireability. In Study 3, we showed that use of a corporate program that enables women to stay in touch with the workplace while on maternity leave (compared to conditions in which no such program was offered; a program was offered but not used by the applicant; and the program was offered, but there was no information about its usage by the applicant) enhances agency perceptions and perceptions of job commitment and hireability. Implications for theory and practice are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".