Taking Workers’ Rights Seriously: Private Prosecutions of Employment Standards Violations
Bibliographic record
Abstract
This paper examines private prosecutions as a tool to challenge the state’s inadequate enforcement of employment standards. In Ontario, poor enforcement of employment standards means that there are no costs for non-compliance, that orders to pay wages are typically not fully paid and that vulnerable workers are left unprotected. Private prosecutions are one tool that could be used by private actors to fight these problems. Historically, private prosecutions have been used where there is a gap in government enforcement, most recently to bring environmental offenders to justice. Privately prosecuting employment standards violations would continue that tradition and would promote compliance through the stigma of criminal proceedings, and by conveying the message that employment standards violations are crimes. This paper discusses the gaps in the enforcement of employment standards in Ontario (section II), explains how private prosecutions can help and how this fits with the historic use of private prosecutions (section III), and describes how private prosecutions of employment standards could be easily implemented (section IV).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".