Unequal Upsides? The Status-Based Inequalities in the Relationship Between Schedule Control and Job Pressure
Bibliographic record
Abstract
ABSTRACT Sociologists have long identified job pressure as a central work-related stressor with far-reaching consequences for workers’ well-being, their families, and organizations. However, surprisingly little empirical work examines how schedule control influences job pressure in a longitudinal framework—or the status-based contingencies in the resource functions of schedule control. Drawing on five waves of population-level panel data from the Canadian Work, Stress, and Health Study (2011–2019), I use fixed-effects analyses to examine the relationship between schedule control and job pressure, examining whether schedule control operates differently across occupations (professionals versus non-professionals) and levels of authority in the workplace. My findings help advance the sociological study of work-stress research by resolving competing predictions about the relationship between schedule control and job pressure across status. While others have argued the possibility for schedule control to intensify work-related pressures, I find that schedule control helps reduce job pressure. However, my results reveal that schedule control does not benefit all workers equally: it has unequal upsides for higher status workers. These discoveries sharpen existing knowledge about the resource functions of schedule control and are discussed in light of synthesizing key ideas from the sociology of work, and the stress process and job demands-resources models.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".