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Trade unions and worker movements in the North American communications industries

2008· article· en· W2755106430 on OpenAlexaff
Vincent Mosco

Bibliographic record

VenueWork Organisation Labour & Globalisation · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsQueen's University
Fundersnot available
KeywordsConsolidation (business)Trade unionWorkforceIndustrial relationsPolitical scienceEconomyBusinessInternational tradeEconomicsAccountingLaw

Abstract

fetched live from OpenAlex

This paper reports on a project that examines trends in North American labour movements, and specifically in the workforce, in the converging communications, culture, and information technology sectors. Drawing on documentary evidence and interviews, the paper concentrates on two important developments: efforts to unify workers across the knowledge and communication industries, and the rise of worker movements that operate in conjunction with, but outside, the formal trade union structure. The paper begins by situating these developments within debates about labour in a ‘post-industrial’, ‘information’, or ‘network’ society. It describes the challenges facing workers in the knowledge sector, especially rapid technological change, massive corporate consolidation, the rise of the neo-liberal state and divisions between cultural and technical workers in the knowledge sector. The paper proceeds to describe how North American workers are responding within the traditional trade union system, primarily through forms of consolidation or trade union convergence (such as the Communication Workers of America), and also through worker movements operating outside the traditional trade union system in the information technology and cultural sectors (for example WashTech and the National Writers Union). The paper concludes by addressing the significance of these developments. Do they portend a rebirth of North American labour activism or do they represent its last gasps?

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.032
GPT teacher head0.283
Teacher spread0.251 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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