Measuring Employment Standards Violations, Evasion and Erosion - Using a Telephone Survey
Bibliographic record
Abstract
For many workers in Ontario, the Employment Standards Act (ESA) provides the only formal measures of workplace protection. The complaints-based monitoring system utilized by the Ontario Ministry of Labour, however, makes it difficult to assess the overall prevalence of employment standards (ES) compliance in the labour force. In addition to outright ESA violations, prevailing research highlights the significance of the erosion, evasion, and outright abandonment of ES for workers’ access to protection through practices such as the misclassification of workers and types of work. In this article, we report on efforts to develop a telephone-survey questionnaire that measures the overall prevalence of ES violations, as well as evasion and erosion in low-wage jobs in Ontario, without requiring respondents to have any pre-existing legal knowledge. Key methodological challenges included developing strategies for identifying ‘misclassified’ independent contractors, establishing measures for determining whether workers were exempt from the ESA , and translating the regulatory nuances embedded in the legislation into easy-to-answer questions. The result is a survey questionnaire unique in the Canadian context. Our questionnaire reflects the concerns of both academic researchers and workers’ rights activists. Pilot survey results show that Ontario workers do not necessarily distinguish between ES violations and other workplace grievances and complaints. With careful questionnaire design, it is nevertheless possible to measure the prevalence of ES violations, evasion and erosion. In order to track the effects of ES policies, particularly those on enforcement, we conclude by calling for the establishment of baseline measures and standardized reporting tools.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".