Opening the Black Box of Work/Family Strategies Facing Low Schedule Control: A Relational Approach
Bibliographic record
Abstract
In the current global context of increasing work intensification and precarious employment, low schedule control is becoming more frequent, often accompanied by low wages and low job control. An increasing number of workers must develop strategies to deal with imposed schedule variability and unpredictability affecting their ability to meet their family responsibilities. Their work/family interface management strategies are impacted by limited access to resources, low control and, often, high job demands. Hence, informal arrangements and support from superiors and coworkers constitute important resources to increase their leeway. In order to address the gap in the literature concerning an understudied group of workers, this paper presents an approach combining ergonomics (work analysis) and communication. It aims to provide insight into the influence of relational dynamics on low-paid and low job-control workers’ work/family strategies. Such an approach has the potential to identify critical environmental determinants such as working conditions and network structures that can potentially enlarge employees’ operational leeway and allow them to deploy their strategies more effectively. This analytical framework considers individual-, workgroup-, and organizational-level determinants, necessarily including gender differences in work activity and interpersonal dynamics.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".