From flexibility to unending availability: Platform workers' experiences of work–family conflict
Bibliographic record
Abstract
Abstract Objective This article examines whether performing location‐based platform work is associated with greater work–family conflict—and if this association is stronger for those relying on labor platforms for their primary employment. Background Digital labor platforms project a vision of flexibility and improved work‐family balance for workers; however, empirical evidence supporting these promises remains elusive. While platform workers are normally offered the freedom to choose their work hours, the efforts of labor platforms to algorithmically manage workers' schedules may encourage an ‘always‐on’ approach to work that pressures workers to prioritize work availability that exacerbates work–family conflicts. Method We conducted three national surveys of Canadian workers in 2020, 2021, and 2022. Based on pooled survey data ( N = 10,483), structural equational modeling was used to investigate (1) the relationship between location‐based platform work and work–family conflict and (2) the mediating role of work‐family role blurring—captured by work contact outside of normal working hours. Results We discovered that platform workers, compared to employees and the traditional self‐employed, reported greater work–family conflict—conflicts that were especially pronounced for those relying on labor platforms as their primary source of income. These patterns were partially explained by platform workers' increased exposure to work contact outside of work hours. Conclusion Our findings question the assertion that digital labor platforms provide enhanced flexibility for managing work and family demands. Instead, we contend that the instability inherent in platform work blurs and disrupts work‐family role boundaries, disproportionately favoring labor platforms and their clientele at the expense of workers' familial responsibilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".