Using tickets in employment standards inspections: Deterrence as effective enforcement in Ontario, Canada?
Bibliographic record
Abstract
<p> It is widely agreed that there is a crisis in labour/employment standards enforcement. A key issue is the role of deterrence measures that penalise violations. Employment standards enforcement in Ontario, like in most jurisdictions, is based mainly on a compliance framework promoting voluntary resolution of complaints and, if that fails, ordering restitution. Deterrence measures that penalise violations are rarely invoked. However, the Ontario government has recently increased the role of proactive inspections and tickets, a low-level deterrence measure which imposes fines of CAD295 plus victim surcharges. In examining the effectiveness of the use of tickets in inspections, we begin by looking at this development in the broader context of employment standards enforcement and its historical trajectory. Then, using administrative data from the Ministry of Labour, we examine when and why tickets are issued in the course of workplace inspections. After demonstrating that even when ticketable violations are detected, tickets are issued only rarely, we explore factors associated with an increased likelihood of an inspector issuing a ticket. Finally, we consider how the overall deterrent effect of workplace inspections is influenced by the use or non-use of deterrence tools.</p>
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".