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Hypermobility and the governance of global production networks: The case of the Canadian cycle industry and its links with China and Taiwan

2012· article· en· W1917560161 on OpenAlexaffvenueabout
Boyang Gao, Weidong Liu, Glen Norcliffe

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsYork University
FundersCentral University of Finance and Economics
KeywordsChinaContext (archaeology)Corporate governanceBusinessOriginal equipment manufacturerHypermobility (travel)Production (economics)Product (mathematics)OutsourcingIndustrial organizationEconomic geographyMarketingEconomicsPolitical scienceGeographyFinance

Abstract

fetched live from OpenAlex

Western firms engaged in mass retailing and in product assembly make frequent changes to their global production networks (GPNs). Indeed, some GPNs show a tendency to hypermobility, which we define as a rapid switching of economic links among manufacturers, importers, and retailers. This theme is explored in the context of the Canadian bicycle industry where domestic production collapsed between 1980 and 2008, following a century of remarkable stability. After a period of flux when Taiwan was the key player, China has emerged as the dominant original equipment manufacturer (OEM) of bicycles sold in Canada, with big‐box stores accounting for the great majority of sales. We connect this increasing fluidity in supply arrangements and in the global organization of the industry with the governance of these GPNs. Several aspects of governance are considered, including the Sloanist practices of the largest Canadian retailers, and the activist role of the Chinese state in directing regional patterns of manufacturing in China.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.065
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0100.014
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations8
Published2012
Admission routes3
Has abstractyes

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