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Union Mobilization: A Consideration of the Factors Affecting the Willingness of Union Members to Take Industrial Action

2008· article· en· W2073075416 on OpenAlexaff
Donna Maree Buttigieg, Stephen Deery, Roderick D. Iverson

Bibliographic record

VenueBritish Journal of Industrial Relations · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInjusticeMobilizationCollective actionAction (physics)Industrial actionWork (physics)CollectivismTrade unionCollective bargainingBusinessPublic relationsPolitical scienceSocial psychologyEconomicsLabour economicsPsychologyIndividualismLaw

Abstract

fetched live from OpenAlex

Abstract Drawing on mobilization theory, this article seeks to identify the factors that shape the willingness of union members to take industrial action. The study utilized data from a large‐scale survey (N = 1,111) carried out in a financial services union during the renegotiation of a collective bargaining contract. The results suggested that individuals were more willing to engage in industrial action when they experienced a sense of injustice or unfairness in the employment relationship and when they held a collectivist orientation to work. Moreover, their propensity to take industrial action was greater when they considered that their union was an effective instrument of power. Workplace representatives were also important, particularly when they were seen as being responsive to their members' needs in situations of perceived injustice. The implications for mobilization theory and for union strategy are discussed.

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)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.109
GPT teacher head0.310
Teacher spread0.201 · 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 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

Citations49
Published2008
Admission routes1
Has abstractyes

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