When Wage Theft Was a Crime in Canada, 1935-1955: The Challenge of Using the Master’s Tools Against the Master
Bibliographic record
Abstract
In recent years the term “wage theft” has been widely used to describe the phenomenon of employers not paying their workers the wages they are owed. While the term has great normative weight, it is rarely accompanied by calls for employers literally to be prosecuted under the criminal law. However, it is a little known fact that in 1935, Canada enacted a criminal wage theft law, which remained on the books until 1955. This article provides an historical account of the wage theft law, including the role of the Royal Commission on Price Spreads, the legislative debates and amendments that narrowed its scope, and the one unsuccessful effort to prosecute an employer for intentionally paying less than the provincial minimum wage. It concludes that the law was a symbolic gesture and another example of the difficulty of using the criminal law to punish employers for their wrongdoing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".