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Record W4402447322 · doi:10.1007/s11199-024-01517-7

Agency Penalties From Taking Parental Leave for Women in Men-Dominated Occupations: Archival and Experimental Evidence

2024· article· en· W4402447322 on OpenAlexafffund
Ivona Hideg, Anja Krstić, Raymond Trau, Yujie Zhan, Tanya Zarina

Bibliographic record

VenueSex Roles · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWilfrid Laurier UniversityYork University
FundersSocial Sciences and Humanities Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsPsychologyAgency (philosophy)Social psychologyDevelopmental psychologySociologySocial science

Abstract

fetched live from OpenAlex

Organizations have started more progressively using and offering family benefits including parental leaves to address the issues of balancing work and family life. Although such leaves are fundamental for supporting, attracting, and retaining women, we examine whether such leaves may also inadvertently affect women's careers in occupations that overly value masculine traits, unless managed carefully. Drawing on the literature on gender stereotypes (micro factors) and occupation gender type (macro factors), we argue that longer (vs. shorter) parental leaves negatively affect women's work outcomes (i.e., annual income, salary recommendation, hireability, and leadership effectiveness) in men-dominated but not in women-dominated occupations because it lowers perceptions of women's agency. We find support for our hypotheses across three studies in the Australian context with an archival data set and two experiments. Our work shows that men-dominated organizational structures reinforce traditional gender stereotypes, whereas such reinforcement does not happen in women-dominated organizational structures. Our research equips leaders and organizations with insights into the unintended negative consequences of parental leave for women. This understanding serves as a crucial first step in developing strategies and programs to mitigate these effects, thereby supporting women in men-dominated occupations and fostering more inclusive and healthy workplaces.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.359
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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