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“We Simply Have to Do that Stuff for our Survival”: Labour, Firm Innovation and Cluster Governance in the Canadian Automotive Parts Industry

2007· article· en· W2164064561 on OpenAlexaffabout
Tod Rutherford, John Holmes

Bibliographic record

VenueAntipode · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsQueen's University
Fundersnot available
KeywordsAutomotive industryAgency (philosophy)RestructuringLegislationCorporate governanceCollective bargainingProduction (economics)Cluster (spacecraft)BusinessOriginal equipment manufacturerIndustrial relationsIndustrial organizationBusiness clusterMarket economyEconomicsLabour economicsEconomic geographyEconomic systemEconomyPolitical scienceSociologyManagementEngineering

Abstract

fetched live from OpenAlex

Abstract Based on a case study of the Canadian Auto Workers (CAW) union in southern Ontario we argue for a critical reconstruction of both the labour geography and industrial cluster literature. The former stresses the active role of labour in the formation of economic landscapes, but has yet to explore labour's agency in production and how labour institutions shape technological change, firm innovation and industrial policy and strategy. Conversely, much of the industrial cluster and regional innovation systems literature is silent on the role of unions and industrial relations institutions in fostering innovation. We conclude with two main points. First, while some contend that positive union roles in innovation can only stem from partnerships with management and team working, we argue that innovation is more likely to emerge and worker interests are better protected when traditional collective bargaining structures and progressive employment legislation play a central role. Second, positive workplace and cluster level cooperation in the Canadian automotive parts industry are jeopardized by the broader and ongoing macro‐economic restructuring of OEM global production networks due to over‐capacity and intense cost‐cutting pressures reverberating down the supply chain.

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)0.002
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 score0.643
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.043
GPT teacher head0.341
Teacher spread0.298 · 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

Citations26
Published2007
Admission routes2
Has abstractyes

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