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Record W2943505423 · doi:10.1108/pr-02-2017-0038

Work-life balance and male lawyers: a socially constructed and dynamic process

2019· article· en· W2943505423 on OpenAlexaffabout
Galina Boiarintseva, Julia Richardson

Bibliographic record

VenuePersonnel Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsWork–life balanceBalance (ability)OriginalitySociologyWork (physics)Sample (material)Value (mathematics)Public relationsQualitative researchSocial psychologyPsychologySocial sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to theorize men’s experiences of work-life balance in male-dominated, high-performance industries. Design/methodology/approach This study provides an in-depth qualitative study comprising interviews and informal conversations with male lawyers in Canada. Findings This study highlights the socially constructed nature of male lawyers’ experiences of work-life balance and the recursive impact of industry, professional and societal expectations and norms. Research limitations/implications A relatively small sample size, suggesting the need for further study with a larger and more diverse sample. The study was conducted in Canada – other national contexts may furnish different results. Practical implications This study identifies the need for greater awareness of how institutional, professional and societal expectations and norms impact on men’s experiences of work-life balance in male-dominated, high-performance industries. Social implications This paper indicates that greater attention needs to be paid to work-life balance among men in male-dominated, high-performance industries. Originality/value This paper explores men’s experiences of work-life balance in a male-dominated industry within an interpretivist paradigm.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.304
Teacher spread0.286 · 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 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

Citations8
Published2019
Admission routes2
Has abstractyes

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