Renorming Labour Law: Can We Escape Labour Law's Recurring Regulatory Dilemmas?
Bibliographic record
Abstract
Historically, protective labour law pushed back against capitalist labour markets by facilitating workers’ collective action and setting minimum employment standards based on social norms. Although the possibilities, limits and desirability of such a project were viewed differently in classical, Marxist and pluralist political economy, each perspective understood that the pursuit of protective labour law would produce recurring regulatory dilemmas requiring trade-offs between efficiency, equity and voice and/or between workers’ and employers’ interests. Recently, some scholars have argued that labour law needs to be renormed in ways that are market constituting rather than market constraining and that this change would avoid regulatory dilemmas. This article reviews the concept of regulatory dilemmas as formulated in the three major traditions of labour law scholarship, critically assesses recent work by Deakin and Wilkinson and by Hyde that proposes to renorm labour law and overcome regulatory dilemmas and proposes an alternative approach to understanding regulatory dilemmas based on the work of Wright.
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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 | 0.070 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.097 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".