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Record W4290725525 · doi:10.3390/merits2030013

Employers’ Perceived Career Impact of Canada’s Parental-Leave Extension from 35 to 61 Weeks—“An Empty Gift”

2022· article· en· W4290725525 on OpenAlexaffabout
Rachael N. Pettigrew

Bibliographic record

VenueMerits · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMount Royal University
Fundersnot available
KeywordsParental leavePerceptionThematic analysisWork (physics)PsychologySick leaveSocial psychologyDemographic economicsPolitical scienceSociologyQualitative researchEconomics

Abstract

fetched live from OpenAlex

Introduced in 1990, Canadian parental-leave policy has seen several iterations. The most recent policy change, introduced in December 2017, extended parental leave from 35 to 61 weeks, resulting in longer work interruptions. Forty-six structured interviews were conducted to explore Canadian employers’ perception of how use of the new extended leave may impact employees’ careers. Though some employers offered explicit support for employees, a large proportion of employers felt that use of the longer leave would negatively impact employees’ careers. The presence of unions appeared to insulate employees from a career impact. A thematic analysis revealed that the career impact perceived by employers resulted from concern for employees’ missed opportunities (e.g., training, promotions), length of absence, specific employment situations (e.g., role, level in the organization, career ambitions, and tenure with the organization), and gendered views of employee leave use. Given that the vast majority of Canadian parental-leave users continue to be women, this research highlights the presence of considerable workplace stigma for work interruptions and that longer parental leave may only serve to exacerbate that stigma, especially for women. Recommendations and implications for parental-leave policy, workers, and employers are discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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.

Study designObservational
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

Citations2
Published2022
Admission routes2
Has abstractyes

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