The Effect of Taking a Paternity Leave on Men’s Career Outcomes: The Role of Communality Perceptions
Bibliographic record
Abstract
Paternity leaves policies, important tools for promoting gender equality that give men an opportunity to care for their newborn children, are becoming increasingly popular and legislated worldwide. However, there has been little research on how paternity leaves impact men’s careers and the research that exists has been inconclusive. This is problematic because, while men are increasingly being encouraged to take paternity leaves, the fear may be that such leaves may undermine their careers. However, by integrating the literature on changing norms regarding effective leadership with expectancy violation theory, we suggest that taking a paternity leave can enhance others’ perceptions of men’s communality and lead to positive career outcomes. We tested our hypotheses in three studies in the context of Canadian parental leave policies. In a sample of undergraduate students (Study 1) and employees (Study 2) we found that increased communality perceptions underlie the positive effect of taking a paternity leave (vs. no paternity leave) on men’s reward recommendations and hireability ratings. In Study 3 we found evidence that the positive effect of paternity leaves on men’s career outcomes was stronger in a female-dominated industry (e.g., human resources) than in a male-dominated industry (finance). Implications for theory and practice are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".