Ambiguities and Absences: Occupational Health and Safety Regulation of Platform-Mediated Work in Ontario, Canada
Bibliographic record
Abstract
Platform-mediated work, whether location-based, as in the case of Uber, or cloud-based, as in the case of Amazon Mechanical Turk, poses severe challenges to effective occupational health and safety (OHS) regulation. While the work performed in the platform environment is not usually very different from work performed in more traditional employment settings, the platform environment often exacerbates those risks by, for example, increasing stress and incentivizing long hours and work intensification. Regulating these hazards is impeded by ambiguities surrounding the legal relationship between platform operators and platform workers that make it uncertain whether the OHS regime even applies. As well the regime itself was not designed to address the conditions of platform work or many of the risks and exacerbating factors it produces. Drawing on existing studies, this article explores the structure of platform-mediated work, examines its incidence in Ontario, Canada, summarizes its associated OHS risks, and provides a detailed analysis of the obstacles to effective regulation under Ontario’s OHS regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".