Unraveling the work–life policies puzzle: How the ‘ideal worker’ norm shapes perceptions of policies legitimacy and use
Bibliographic record
Abstract
The development of work–life policies—e.g., employee assistance programs, on-site childcare, flextime, part-time, compressed week, and so on—is increasingly important for a growing number of organizations. Though such programs provide benefits for both employees and employers, usage rates are still low. Scholars have called for research that addresses this phenomenon and more particularly explains the underlying processes of individual decision-making concerning work–life balance, and describe why and how certain social groups differ in their approaches to policy use. Our inductive study –based on 44 individual interviews- aims to address these issues. We found that the policies are used differently depending on the employees’ social group, and that certain salient social identities—such as gender, parenthood and managerial status—shape their use. Such programs are a structural and cultural change for organizations and often present an opportunity for redefining the centrality of work. Indeed the values inherent in them, including resting and taking time for oneself or for one’s family, may conflict with the traditionally masculine values associated with the ‘ideal worker’, intuitively linked to performance and production of positive results. The clash between the two, which permeated the interviews, causes employees to fall back on the social identity or identities they find meaningful. Our findings show three main strategies that individuals use when they feel that their social identity is threatened: (1) engage in workaround activities to avoid using work-life policies; (2) try to compensate for policies use (by engaging in projects outside one’s job or doing overtime work) ; and (3) significantly limit policies use. These results contribute to literature by showing that many managers and men do not feel legitimate to use work-life policies and find workarounds to manage without them, thus perpetuating stereotypical masculine norms. We demonstrate that the identity threat that underlies work-life policies taking may help women in the short term, but also contributes to their discrimination in the long run as well as is detrimental to the work-life balance of men.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".