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Record W4365797106 · doi:10.58188/1941-8043.1867

A Different Set of Rules? NLRB Proposed Rule Making and Student Worker Unionization Rights

2020· article· en· W4365797106 on OpenAlexaboutno aff
William A. Herbert, Joseph van der Naald

Bibliographic record

VenueJournal of Collective Bargaining in the Academy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
FundersGraduate CenterCity University of New York
KeywordsCollective bargainingLabor relationsEmpirical evidencePrivate sectorLabour lawEconomicsPolitical scienceUnited States labor lawLabour economicsState (computer science)Public relationsLaw

Abstract

fetched live from OpenAlex

This article presents data, precedent, and empirical evidence relevant to the National Labor Relations Board (NLRB) proposal to issue a new rule to exclude graduate assistants and other student employees from coverage under the National Labor Relations Act (NLRA). The analysis in three parts. First, the authors show through an analysis of information from other federal agencies that the adoption of the proposed NLRB rule would exclude over 81,000 graduate assistants on private campuses from the right to unionize and engage in collective bargaining. Second, the article presents a legal history from the past half-century about unionization of student employees at private and public sector institutions of higher education, including the NLRB’s oscillation on the question of whether student employees are protected under the NLRA. The inconsistencies of the NLRB is in stark contrast to state and Canadian provincial precedent during the same period.. Lastly, the authors analyze the terms of 42 current collective bargaining agreements covering student workers, including 10 at the private sector institutions. The empirical evidence from five decades of relevant collective bargaining history, precedent, and contracts demonstrates consistent economic relationships between student employees and their institutions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.344
Teacher spread0.302 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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