Workers’ Boards: Sectoral Bargaining and Standard-Setting Mechanisms for the New Gilded Age
Bibliographic record
Abstract
This article explores the potential of sectoral standard-setting models (often referred to as “wage boards” or “workers’ boards”) as a solution for contemporary workplace issues, which existing labor relations and minimum standards regulatory systems continue to struggle to address. This argument, the article examines three historical statutory systems of sector-based minimum workplace standard-setting established in the early 20th century as a response to unacceptable wages and working conditions: the British Wages Council system, the Canadian Industrial Standards Act, and the US Fair Labor Standards Act. The article applies the conceptions of fairness identified in Seth Harris's study of the origins of the Fair Labor Standards Act to analyze the three systems and offers a three-step approach to constructing a sectoral workplace standard-setting mechanism. This paper contributes to the ongoing discourse on worker representation and workplace standards by offering a conceptual starting point for designing a sectoral workplace standard-setting mechanism. The article highlights key design decisions and alternatives and maps out essential interrelated considerations, providing valuable insights for policymakers and stakeholders seeking to improve worker representation and workplace standards.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".