Challenging new governance: Evaluating new approaches to employment standards enforcement in common law jurisdictions
Bibliographic record
Abstract
A mounting crisis in employment standards (ES) enforcement is prompting the adoption of new instruments and mechanisms among governments in common law jurisdictions aiming to improve workplace regulation. This shift, evident across all stages of the enforcement process, indicates the increasing influence of regulatory new governance. Using reforms in four jurisdictions as illustrative examples, this article raises serious cautions around the emergence of regulatory new governance in employment standards enforcement. The central argument of the article is that new modes of regulation that fail to account adequately for the power dynamics of the employment relationship risk entrenching processes of regulatory degradation. In light of this potential, the article outlines four principles for more effective ES regulation that aim to balance aspects of traditional regulatory models with a selective application of more promising elements of regulatory new governance, in particular participatory arrangements that involve workers in enforcement processes.
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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 | 0.091 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".