Private Injuries, Public Policies: Adjusting the NLRB's Approach to Backpay Remedies Symposium: Whither the Board: The National Labor Relations Board at 75
Bibliographic record
Abstract
From fiscal years 2004 through 2008, over 135,000 employees received backpay through NLRB proceedings, mostly based on wrongful discharges. The Labor Board's backpay determination processes are often cumbersome and time-consuming to apply: they effectively invite employers to reduce and delay monetary recoveries and, not coincidentally, they undermine the remaining employees' interest in pursuing unionization and a collective bargaining relationship. The Article first asks to what extent the Board has statutory authority to adjust its approach toward backpay and mitigation. The answer, in short, is more than has previously been understood. Invoking the remedial authority found within section 10(c) and embraced by the Supreme Court in its Phelps Dodge decision, the Article then proposes that the Board act to develop and defend a mandatory minimum backpay award. The Article proposes a two-tiered approach, based on the substantial differences in processing time between successful backpay claims resolved through settlement and claims resolved following litigation. The Article explains and justifies the award of mandatory minimum backpay ranging from one calendar quarter to one year, to be conferred without regard to net loss and mitigation effects.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.006 |
| 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".