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Record W2143779822 · doi:10.1504/ijtlid.2009.021955

The North American automotive value chain: Canada's role and prospects

2008· article· en· W2143779822 on OpenAlexaffabout
Timothy J. Sturgeon, Johannes Van Biesebroeck, Gary Gereffi

Bibliographic record

VenueInternational Journal of Technological Learning Innovation and Development · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutomotive industryInvestment (military)Production (economics)Value (mathematics)SustainabilityBusinessEconomyGlobal value chainMarket shareChinaIndustrial organizationEconomicsInternational tradeEngineeringComparative advantageGeographyPolitical scienceMarketing

Abstract

fetched live from OpenAlex

This paper deals with the North American automotive value chain and analyses the prospects for Canadian automotive sector upgrading. The size and importance of the automotive industry in Canada's Ontario Province is a legacy of its historic ties to the 'Big 3' US automakers and its proximity to the traditional heartland of the US industry in Michigan. Canada continues to have marginally lower operating costs than the USA and a strong industrial culture that attracts investment. But Mexico's integration into the North American production system, the rise of new centres of automotive production in the southern USA and rapidly growing flow of automotive parts from China to North America have begun to erode this advantage. Because the North American market is saturated, consisting mainly of sales of replacement vehicles, locational shifts in production and employment within North America are essentially 'zero-sum games'. If the market share of the Big 3 continues to fall and the southward shift of the industry within the USA is maintained, the sustainability of the Canadian industry could be undermined. The paper concludes with a set of policy recommendations for Canada to maintain its comparative advantage in the industry.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.203
Teacher spread0.177 · 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

Citations16
Published2008
Admission routes2
Has abstractyes

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