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Record W3174962988 · doi:10.1590/0101-31572001-1229

Silicon Valley in the South

2001· article· en· W3174962988 on OpenAlexaboutno aff
Mitsuhiro Kagami, Akifumi Kuchiki

Bibliographic record

VenueBrazilian Journal of Political Economy · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationOutsourcingBusinessCommerceProcurementSupply chainGlobalizationProduction (economics)Product (mathematics)Industrial organizationLatin AmericansFunction (biology)Service (business)LiberalizationInternational tradeMarketingMarket economyEconomicsFinance

Abstract

fetched live from OpenAlex

ABSTRACT New trends are now taking place within manufacturing industries led by multi-national corporations (MNCs). Globalization and liberalization together with the information technology (IT) revolution has accelerated “fables” industry in the network economy, i.e. outsourcing production processes and global parts procurement by MNCs. As a consequence of this, the primary function of the MNC has changed from that of manufacturer to ‘service’ provider by outsourcing production processes to foreign contract manufacturers (CMs). NAFTA in fact mutated Mexico into a production platform toward the US and Canada as well as Latin American countries. We can observe these dramatic changes, for instance, in Guadalajara in Mexico, now called the “Silicon Valley in the South”. Since MNCs use their brand names to sell products, their business function becomes close to that of the fashion industry. They market their products in the same way as Gucci and Chanel sell products of original design carrying their brand names. Therefore, product design and marketing become highly important for MNCs to achieve success in business while domestic providers have been left behind for their parts and components supply in this new global 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)0.001
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: none
Teacher disagreement score0.729
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.304
Teacher spread0.271 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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