Solving the Problem from Hell: Tripartism as a Strategy for Addressing Labour Standards Non-Compliance in the United States
Bibliographic record
Abstract
The crises of wage theft and industrial accidents in low-wage America reflect erosion of the social contract but they also reflect a crisis in labour standards enforcement. This article draws upon archival material, case studies, and interviews to make the case for tripartism—an enforcement regime that partners workers’ organizations with government inspectors to patrol workers’ industries and labour markets for unfair competition. It extends to the federal level previous work in which Jennifer Gordon and i have documented dynamic contemporary examples of tripartism at the state and local levels. The article explores historical precedents for tripartist collaboration on the federal level at the Department of Labor (DOL) in the Wage and Hour Division and the Occupational Safety and Health Administration. It then considers several tripartist initiatives at the DOL under the Obama administration, the legal obstacles that purportedly stand in the way of more robust approaches, and some potential solutions. The article concludes with an explanation of why formalizing partnerships matters.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.028 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.013 |
| 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".