Who Gets to ‘Work Hard, Play Hard’? Gendering the Work–Life Balance Rhetoric in Canadian Tech Companies
Bibliographic record
Abstract
This article is based on an exploratory study of the implicit gender norms in work–life balance (WLB) rhetoric in ten Canadian information and communication technologies (ICT) organizations. Interviews with human resources (HR) managers and preliminary company website analysis revealed a masculinist and heterosexist bias in the implementation of WLB practices, legitimized by the gender composition of the workforce and the demanding yet inherently rewarding nature of the ICT sector. Participants deliberately separated care (read: childcare) from WLB (read: flexible hours and working from home), reproducing the assumption that an ‘ordinary’ worker is a man with a female partner who assumes primary responsibility for the reproductive realm. The study concludes with: (i) recommendations to increase HR's role in providing functional support for WLB practices and (ii) three future directions for research. This article contributes to a general call in feminist scholarship to apply a gendered lens to WLB practices.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.049 | 0.033 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".