Bibliographic record
Abstract
This paper examines whether flexible work schedules in Canada are created by employers for business reasons or to assist their workers achieve work-life balance. We focus on long workweek, flextime, compressed workweek, variable workweek length and/or variable workweek schedule. In the last three decades, two streams of literature have emerged on flexibility. One stream of literature discusses flexibility as demand-driven, that is, a strategic initiative of employers to enhance the business requirements of the firm. The other stream of literature discusses flexibility as supply-driven, where employees have the ability to influence the decisions about the nature of their work schedules and where employees, especially women, demand flexible work schedules for work-life balance. Thus, we ask are flexible work schedules created for business reasons or to assist workers achieve work-life balance? Statistics Canada’s 2003 Workplace and Employee Survey data linking employee microdata to workplace (i.e., employer) microdata are used in the analysis. Results show that more than half of the workers covered in this data have at least one of the five specified types of flexible work schedules. Approximately 5% of workers have a long workweek, 36% have flextime, 7% a compressed workweek, 13% a variable workweek length, and 16% a variable workweek schedule. Only two in five Canadians have a standard work schedule. Employment status, unionized work, occupation, and sector are factors consistently associated with flexible work schedules. Personal characteristics of marital status, dependent children, and childcare use are not significantly associated with flexible work schedules, while females are less likely to have a flexible work schedule than males. Overall, results suggest that flexible work schedules are created for business reasons rather than individual worker interests. Thus, if public policy makers are committed to facilitating workers’ work-family-life balance interests, then our results suggest that separate policy initiatives designed specifically for workers will be required.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.029 |
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".