The Effect of Taking a Paternity Leave on Men’s Career Outcomes - Student Sample
Bibliographic record
Abstract
Integrating the literature on changing norms regarding effective leadership with expectancy violation theory, we suggest and have previously found that taking a paternity leave can enhance others’ perceptions of men’s communality, which are in turn related to positive career outcomes. In a sample of students at a Canadian university, we further test whether taking a parental leave (vs. no parental leave) may lead to enhanced communality perceptions for men, which in turn, are related to positive workplace outcomes (e.g., hireability, reward recommendations, and leadership effectiveness). In addition to comparing the workplace outcomes of men who took a parental leave to men who did not take a parental leave, we will also compare them to women who took a parental leave and women who did not take a parental leave. We do not expect to find beneficial effects for women (especially for longer parental leaves). Further, we also examine whether anticipated task-, relational-, and change-oriented leadership behaviors act as potentially competing underlying mechanisms in the effect of taking a parental leave (vs. no parental leave) on the workplace outcomes (e.g., hireability, reward recommendations, and leadership effectiveness) of men and women. This preregistered experiment was created after an initial round of peer review feedback on our first three studies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".