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Record W2963359436 · doi:10.1108/ijm-10-2018-0313

Exploring the link between sexual orientation, work-life balance satisfaction and work-life segmentation

2019· article· en· W2963359436 on OpenAlexaffabout
Maryam Dilmaghani

Bibliographic record

VenueInternational Journal of Manpower · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsLife satisfactionSexual orientationWork–life balancePsychologyOperationalizationJob satisfactionSocial psychologyCore self-evaluationsWork (physics)Scale (ratio)Developmental psychologyJob attitudeGeographyEngineeringJob performance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to, using several cycles of the Canadian General Social Survey (GSS) covering 2010–2015, examine the patterns of work-life balance (WLB) satisfaction and work-life segmentation by sexual orientation. Design/methodology/approach In this paper, multivariate regression analysis is used. Findings The analysis shows that men living with a male partner are more satisfied with their WLB than their heterosexual counterparts. No statistically significant difference is found between women who live with a female partner and their heterosexual counterparts, in WLB satisfaction. Work-life segmentation is operationalized by the odds of being at the top levels of the life satisfaction scale without being satisfied with the circumstances of one’s job. Controlling for a wide range of characteristics, working Canadians living with a same-sex partner, regardless of their genders, are found more likely to have segmented their work and life domains than their heterosexual counterparts. Originality/value The paper, for the first time, investigates how sexual orientation relates to WLB satisfaction and work-life segmentation. This study exploits a unique opportunity offered by the Canadian GSSs in which WLB satisfaction is directly surveyed, all the while partnered sexual minorities are identifiable.

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 score0.034
Threshold uncertainty score0.400

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 communication0.0000.001
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 head0.065
GPT teacher head0.324
Teacher spread0.259 · 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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