A Model Regulator? Investigating Reactive and Proactive Labour Standards Enforcement in Canada’s Federally Regulated Private Sector
Bibliographic record
Abstract
This article examines labour standards violations and enforcement activities in Canada’s federally regulated private sector (FRPS) between 2006 and 2018. Drawing on an administrative data set (known as the Labour Application 2000 (LA2K)) from the federal Labour Program of Employment and Social Development Canada – we illustrate the dominance of a complianceoriented approach to labour standards enforcement in the federal labour inspectorate. This compliance- oriented model of enforcement assumes that most labour standards violations result from lack of knowledge on the part of employers, and that violations are exceptional rather than a regular feature of contemporary business practices geared to cost-containment. Further, the dominance of a compliance-based enforcement strategy is rooted in the historically unique working conditions, industrial composition, and social demographics of the FRPS. In short, the sector has been characterized historically by a disproportionate number of large firms, and a highly male-dominated workforce, engaged in full-time permanent employment. However, numerous labour standards violations are evident in growing pockets of precarious employment, particularly among small firms in the trucking sector. We argue that the litmus test for the regime’s efficacy should be the degree to which it serves employees in the most precarious employment situations. The inspectorate devotes relatively little time to proactive workplace inspections. Those violations that inspectors do uncover through proactive inspections are principally non-monetary and are rectified primarily on the basis of securing employers’ written commitments to bring their practices into compliance with minimum standards. By way of conclusion, the article outlines the ways in which reliance on a compliance model of enforcement in the FRPS may be contributing to the erosion of labour standards, particularly for those workers in industries where small firms dominate and precarious employment is concentrated, and calls for a more deterrence-oriented approach. Labour Standards, Enforcement, Compliance, Violations, Workplace Inspections, Federal Jurisdiction, Canada
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".