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Record W3036690486 · doi:10.1080/01436597.2020.1761252

Automotive global value chains in Mexico: a mirage of development?

2020· article· en· W3036690486 on OpenAlexaff
Mateo Crossa, Nina Ebner

Bibliographic record

VenueThird World Quarterly · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBackwardnessAutomotive industryDeindustrializationCapital (architecture)EconomicsArgument (complex analysis)Value (mathematics)EconomyInternational tradeBusinessEconomic growthGeographyEngineering

Abstract

fetched live from OpenAlex

International monetary organisations argue the ‘developing countries’ should foster linkages to the world economy as a means to overcome backwardness. In this article we refute the narrative that Mexico has experienced industrial upgrading. Rather, industrial growth in Mexico over the last 40 years has been shaped by neoliberal economic policies which have turned the Mexican economy into an export-led manufacturing platform designed to supply the North American market, sustained by a precarious labour market. As a result, Mexico occupies the most labour-intensive and low value-added segments of regional production chains. To make this argument, we perform an in-depth analysis of the Mexican automotive industry, demonstrating that instead of being an engine for domestic industrial development, the auto industry has become a dominant economic sector through productive hyper-specialisation concentrated in the northern Mexican border states, a reliance on transnational capital, particularly from the United States, a disconnect with domestic markets, and the super-exploitation of labour.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.253
Teacher spread0.234 · 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 designNot applicable
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

Citations51
Published2020
Admission routes1
Has abstractyes

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