Not All Work-Life Policies Are Created Equal: Career Consequences of Using Enabling Versus Enclosing Work-Life Policies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many employees hesitate to use work-life policies (e.g., flexible work arrangements, leave, on-site services) for fear of career consequences. However, findings on the actual career consequences of such use are mixed. We debundle work-life policies, which we view as control mechanisms that may operate in an enabling way, giving employees some latitude over when, where, and how much they work, or in an enclosing way, promoting longer hours on work premises. Drawing on signaling and attributional theories, we construe the nature of the policies used as a work devotion signal; specifically, we argue that supervisors attribute lower work devotion to employees who use more enabling policies than to employees who use more enclosing policies. However, this relationship is moderated by employees’ work ethic prior to the use, by supervisors’ expectations of employees, and by the family supportiveness of organizational norms. In turn, the work devotion attributions made by supervisors lead to positive and negative career consequences for work-life policies users, depending on organizational norms. Our model opens up new avenues of research on the work-life policies implementation gap by differentiating between the policies and by teasing out the roles played by policies, organizational norms, supervisors, and employees.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it