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Record W2483478603 · doi:10.1057/9780230599451_8

Conclusion: Understanding Japan’s Innovation Strategies

2007· book-chapter· en· W2483478603 on OpenAlexaff
Carin Holroyd, Ken Coates

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2007
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsCentre for International Governance InnovationUniversity of WaterlooAsia Pacific Foundation of Canada
Fundersnot available
KeywordsGovernment (linguistics)Position (finance)Variety (cybernetics)Political sciencePlan (archaeology)The InternetEngineeringBusinessGeographyComputer science

Abstract

fetched live from OpenAlex

The study of comparative national innovation strategics presents formidable challenges. In this book, we have described a wide variety of government, corporate and academic initiatives designed to advance Japan’s competitive position and to create the foundations for a 21st century economy. The individual initiatives — from massive science cities to pet robots and the mobile Internet revolution — are fascinating but far from definitive proof that Japan is, indeed, the most innovative nation in the world. Statistics on government and corporate expenditures on research and development are instructive but only part of a complex story. So, too, is the substantial revamping of the Japanese university system, and the domestic use of mobile internet. Major commitments to basic science research show that Japan is hedging its bets on the future directions of science and is, more aggressively than other countries, exploring the commercial possibilities of nanotechnology and biotechnology. It is an important, and even impressive, account of a nation’s zeal of science and technology-based innovation, but does not necessarily outline an assured plan of Japanese economic success in the coming decades.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.253
Teacher spread0.176 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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