Work-life balance and male lawyers: a socially constructed and dynamic process
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".