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Geography, Learning, and Convergence

2004· book-chapter· en· W3098562187 on OpenAlexaboutno aff
Meric S. Gertler

Bibliographic record

VenueOxford University Press eBooks · 2004
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsSovereigntyMultinational corporationConvergence (economics)GlobalizationPolitical scienceLeverage (statistics)Economic systemEconomyPolitical economyEconomicsMarket economyInternational tradePoliticsEconomic growthFinance

Abstract

fetched live from OpenAlex

According to an increasingly accepted view, the sovereignty of national economies has been eroded to the point where nation-states ‘have become little more than bit actors’ (Ohmae 1995: 12). With the development of globalized financial markets, the rising power of multinational corporations (MNCs), and the emergence of a new set of supranational institutions to govern economic processes on a continental or world scale, nation-states are said to have lost the ability to manage their own domestic economic affairs, having ceded control over exchange rates, investment, and even fiscal policy to extra-national forces (Strange 1997). Moreover, with the increasing leverage and reach of MNCs further contributing to the erosion of national economic sovereignty, the once distinctive character of particular national industrial ‘models’ is said to be under imminent threat. While it may still be possible to identify at least three clearly distinctive national models—an Anglo-American model, a Rhineland (German) model, and a Japanese model—the decline of national institutions, the intensification of competitive forces on a global scale, and the cross-penetration of national markets by MNCs are said to have propelled a process of convergence between these different national models (see Martin and Sunley 1997 for a review of these arguments). In most representations of this globalization dynamic, convergence is regarded as inexorable. One of the most important processes underpinning this dynamic is learning. At the global level, large corporate actors are allegedly learning from each other, so that the most successful corporate practices are emulated and diffused cross-nationally at an increasingly rapid pace. In the late 1980s and early 1990s, considerable attention was devoted to the diffusion of methods of production and workplace organization perfected by Japanese producers of cars and consumer electronics, in which American, Canadian, and European manufacturers were shown to be learning methods such as just-in-time, kaizen/continuous improvement, and other aspects of ‘lean production’ techniques from their Japanese competitors (Womack, Jones, and Roos 1990). With the resurgence of the United States’ economy in the second half of the 1990s, American practices have apparently become the object of global firms’ affections, with large corporations in Europe and Asia adopting the core characteristics of US-style ‘shareholder capitalism’: especially flexible labour market practices, ‘re-engineering’, and the empowerment of shareholders (The Economist 1996a; 1996b).

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0040.038
Scholarly communication0.0080.010
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.016
GPT teacher head0.183
Teacher spread0.166 · 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 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".

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Citations0
Published2004
Admission routes1
Has abstractyes

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