Clean Slate and the Wagner Model: Comparative Labor Law and a New Plurality
Bibliographic record
Abstract
Ever since Canada imported the basic features of the U.S. Wagner Act in the 1940s there has been a natural tendency for academics and labor policy-makers to track cross-border developments. This pattern continues with the recent release of the Clean Slate for Worker Power report out of Harvard Law School. Many of the proposals to strengthen the Wagner Model found in Clean Slate are now or have in the past been law in parts in Canada. However, Clean Slate also argues that it is not enough to strengthen the Wagner Model, because even at its peak of effectiveness, that Model excluded millions of the most vulnerable workers in our two countries. A lesson from Clean Slate, which has also been advocated in some corners within Canada, is that it is possible to preserve and strengthen the Wagner Model for those sectors of the economy where it works, while also advocating and constructing new models to extend collective bargaining to sectors where the Wagner Model has never and will never reach. This is a call for a new plurality in collective bargaining law and policy.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.052 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".