Attacks on Public-Sector Bargaining as Attacks on Employee Voice: A (Partial) Defence of the Wagner Act Model
Bibliographic record
Abstract
The attacks on public-sector union rights in the United States that began in 2011 are one of the most important developments in labour law in recent memory. These events shed light on employee voice issues, and on the continuing viability of the “Wagner Act” model. While declining union density rates in the private sector have prompted some to question this model, high-density rates in the public sector show that unions can flourish under it. This article gives an overview of public-sector unions in the US and summarizes the recent attacks on their rights. It then addresses rulings in both Missouri and Canada that found constitutional rights to collective bargaining, decisions that leave those rights intriguingly undefined. It concludes that advocates of employee voice should understand that, in the current political climate, those unsympathetic to employee voice will have significant clout in developing alternatives to the Wagner Act model.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".