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Record W4243243008 · doi:10.1504/ijatm.2017.084804

The commoditisation of automotive assembly: Canada as a cautionary tale

2017· article· en· W4243243008 on OpenAlexaffabout
Greigory Mordue, Brendan Sweeney

Bibliographic record

VenueInternational Journal of Automotive Technology and Management · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAutomotive industryIncentiveProduction (economics)Competitor analysisInvestment (military)Quality (philosophy)Industrial organizationBusinessInternational tradeEconomicsMarket economyInternational economicsEngineeringMarketingPolitical science

Abstract

fetched live from OpenAlex

For generations, automotive manufacturing has made an outsized contribution to the Canadian economy. The industry's growth was supported by active industrial policy, including an evolving combination of incentives: low-cost, high-quality labour, access to the largest market in the world, and investment inducements; and deterrents: market access and trade-related levers and threats. In recent years, many of the advantages Canada made for itself have eroded. The rise of new competitors, shifting philosophies surrounding trade, cost pressures, and a new paradigm with respect to investment incentives have challenged policy-makers. Several attributes previously considered the preserve of advanced countries like Canada are now evident in lesser developed jurisdictions, a phenomenon that this paper terms the commoditisation of automotive assembly. It is the result of standardised production processes and the automotive industry's ability to recruit top tier talent, particularly in less developed countries. This phenomenon is evident in the convergence of quality results and capital intensity across geographic boundaries and economic strata, expanding production in lesser developed countries, the emergence of luxury vehicle production in those countries, and stagnation of final assembly production in developed countries.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.920

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 science0.0010.000
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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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 designTheoretical or conceptual
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

Citations12
Published2017
Admission routes2
Has abstractyes

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