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Record W2054358827 · doi:10.15173/glj.v6i1.2455

Organizing at Walmart: Lessons from Quebec's Women

2015· article· en· W2054358827 on OpenAlexaffvenueabout
Stéphanie Mayer, Yanick Noiseux

Bibliographic record

VenueGlobal Labour Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsContext (archaeology)Representation (politics)NewspaperWork (physics)Trade unionSection (typography)Political scienceSociologyJob marketPublic relationsLabour economicsLawBusinessEconomicsAdvertisingEngineeringHistory

Abstract

fetched live from OpenAlex

The job market has undergone fundamental changes over the last thirty years. The decline in permanent, stable, full-time employment and an increase in the number of non-standard workers transformed workers' collective organization and union representation. Based on a study of the collective struggles of women employed at Walmart in Quebec and taking into consideration the interrelated effects of non-standard workers' work and living conditions, the paper explores the ways in which organized labour can adapt to the new context. Two types of data were used: newspapers and academic literature and the results of a study in which eleven women working at Walmart were interviewed between 2010 and 2012. This case study is divided into three parts. In the context of the tertiarization of Quebec's employment market, the effects of the flexibilization of labour at Walmart are first presented through statistical evidence and the comments of the women interviewed. The next section provides an overview of the UFCW's union battles with Walmart, including some of the more successful campaign strategies. The final section focuses on participant testimonies to examine what can be learned from these union experiences in the hopes of contributing, insofar as possible, to the discussion on union renewal.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.332
Teacher spread0.293 · 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.

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

Citations4
Published2015
Admission routes3
Has abstractyes

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