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Record W2020417807 · doi:10.1177/0022185613507165

US union revival, minority unionism and inter-union conflict

2013· article· en· W2020417807 on OpenAlexaff
Mark Harcourt, Helen Lam, Geoffrey Wood

Bibliographic record

VenueJournal of Industrial Relations · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsAthabasca University
Fundersnot available
KeywordsRepresentation (politics)LegislatureCertificationCompetition (biology)Political sciencePolitical economyPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

One option for reversing US union decline, requiring no legislative change, would involve re-legitimizing non-majority or minority union representation, allowing unions to organize without running the gauntlet of union certification. Such minority representation, applicable only to workplaces without majority union support on a members-only basis, could run in parallel with the existing system of exclusive representation in workplaces where majority support is achieved. The increased representation in the currently unrepresented workplaces would inevitably promote workers’ collective voice and contribute to union revival. However, minority unionism has been criticized for breeding union competition because it is non-exclusive. In this paper, the nature and extent of inter-union conflict under minority unionism are re-examined, using survey data from unions in New Zealand which already has non-exclusive, minority union representation. The low levels and consequences of conflict suggest that the benefits of minority unionism far outweigh any potentially unfavourable 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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.057
GPT teacher head0.312
Teacher spread0.255 · 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 designQualitative
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

Citations9
Published2013
Admission routes1
Has abstractyes

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