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Record W6986502169

Private Injuries, Public Policies: Adjusting the NLRB's Approach to Backpay Remedies Symposium: Whither the Board: The National Labor Relations Board at 75

2009· article· en· W6986502169 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsStatutory lawCollective bargainingSupreme courtSettlement (finance)Labor relationsRemedial educationQuarter (Canadian coin)Industrial relations
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0130.004
Open science0.0020.004
Research integrity0.0130.006
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.029
GPT teacher head0.250
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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