MétaCan
Menu
Back to cohort
Record W4206521211 · doi:10.31235/osf.io/t572s

More Time with the Family? Workplace Flexibility Policies and Fathers’ Time with Children

2021· preprint· en· W4206521211 on OpenAlexafffund
Dana Wray

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsFlexibility (engineering)Family-friendlyInequalityResource (disambiguation)Control (management)PsychologyParental leaveWork (physics)Developmental psychologyEconomicsComputer scienceEngineeringManagement

Abstract

fetched live from OpenAlex

Workplace flexibility policies, which provide control over the timing and location of work, are a family-friendly resource that may facilitate increased father-child time. Yet, research on this relationship often focuses narrowly on “childcare time,” which not only overlooks the majority of time fathers spend with their children, but also neglects fathers’ accessibility to and responsibility for children as well as the co-presence of the mother. This limits our understanding of how flexibility policies might enhance family well-being and mitigate persistent gender inequalities. Using the 2017-2018 American Time Use Survey Leave Module, this study examines the relationship between flextime (control over start and end times) and flexplace (working from home) policies and different-sex partnered fathers’ time with children. Access to and use of flexibility policies are associated with more family time with children – when the mother is also present. This includes not only time in childcare but also other activities such as meals as well as supervision or accessibility. However, there are no differences in fathers’ solo parenting time. Ultimately, these findings elucidate a more comprehensive picture of how flexibility policies might shape father involvement, and complicate our understanding of the consequences of flexibility for family well-being and gender inequality.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.280
Teacher spread0.261 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicWork-Family Balance ChallengesFrench-language works237,207