On-Demand and Marketplace Platforms: Gig Care Work Conditions on Two Digital Labour Platform Care Models
Bibliographic record
Abstract
With worker shortages and the need for care workers projected to grow, personal care work through digital labour platforms (DLPs) is important to understand. This paper considers DLPs used by gig care workers providing personal care in Ontario, Canada. We recruited 20 women gig care workers for interviews. We examined socio-technical processes such as signing up on platforms, creating profiles, and searching for jobs. We draw on Institutional Ethnography to study the actual work on the following two DLP models: marketplace and on-demand. Marcusean theory provides a lens for the critical examination of DLP care work. We found job inequity between DLPs operating in the homecare sector, compared to DPs used in institutional settings. Jobs had disparate quality between the two platform types. Workers on both DLP types remained vulnerable to fluctuations in demand and had limited social security protections, and both models of DLP institutionalised precarity.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".