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Record W2255249129 · doi:10.1002/alt.21615

Cutting Arbitration Classes: Facing Court Defeats on Workplace Waivers, the NLRB Refuses To Back Down

2016· article· en· W2255249129 on OpenAlexaboutno aff
Russ Bleemer

Bibliographic record

VenueAlternatives to the High Cost of Litigation · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersAmicus Therapeutics
KeywordsArbitrationSupreme courtQuarter (Canadian coin)Class actionLawAction (physics)Class (philosophy)Political scienceBusinessCompulsory arbitrationFederal Arbitration ActLaw and economicsEconomicsState (computer science)Computer scienceHistory

Abstract

fetched live from OpenAlex

If 2015's final quarter is viewed as a leading indicator, the New Year's ADR landscape will be dominated by legal brawling over mandatory class‐action waivers in employment arbitration. And the current battles put the issue closer to a conclusive U.S. Supreme Court decision on whether employers can force workers to agree to forego class actions in court or arbitration as a condition of taking their jobs.

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)0.037
metaresearch head score (Gemma)0.067
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0200.009
Scholarly communication0.0360.013
Open science0.0050.012
Research integrity0.0520.035
Insufficient payload (model declined to judge)0.0230.006

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 head0.027
GPT teacher head0.312
Teacher spread0.285 · 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
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAlternatives to the High Cost of LitigationSame topicLabor Movements and UnionsFrench-language works237,207