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The workplace: challenges for fathers and their use of leave

2019· book-chapter· en· W2938031295 on OpenAlexaboutno aff
Valérie Harvey, Diane‐Gabrielle Tremblay

Bibliographic record

VenuePolicy Press eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsOvertimeParental leaveExploratory researchFamily LeaveWork (physics)Paid workPsychologyWork hoursDemographic economicsLabour economicsWorking hoursEconomicsSociologyEngineering

Abstract

fetched live from OpenAlex

Since 2006, the Québec Parental Insurance Plan has given fathers in this Canadian province the opportunity to take three to five weeks of paid Paternity Leave during the first year after the birth of a child; they can also use up to 25 or 32 weeks of Parental Leave, depending on the option chosen. Two exploratory qualitative research studies of fathers show that taking a Paternity Leave of five weeks is well accepted within the workplace, but the timing of the leave can be perceived as problematic. But fathers who choose to remain at home beyond the Paternity Leave must make more compromises with their employer, particularly, as this study shows, in the IT multimedia sector. They are often the first in their workplace to ask for and to take Parental Leave and can become an inspiration for other employees, but when they return to work, it can be difficult to follow the same rhythm as before and to be present for the same hours as others, especially when it comes to overtime. Faced by such difficulties, some fathers even reported changing their employer in order to better reconcile their work with their new family situation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.003
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.123
GPT teacher head0.321
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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