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Econometrics

2002· book· en· W4231915120 on OpenAlexaboutno aff
Dale W. Jorgenson

Bibliographic record

VenueThe MIT Press eBooks · 2002
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityScope (computer science)ChinaInvestment (military)Division of labourEconomicsDevelopment economicsInternational tradeEconomyEconomic growthPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

Studies of the relation between information technology and economic growth trends.The relentless decline in the prices of information technology (IT) has steadily enhanced the role of IT investment as a source of economic growth in the United States. Productivity growth in IT-producing industries has gradually risen in importance, and a productivity revival has taken place in the rest of the economy. In this book Dale Jorgenson shows that IT provides the foundation for the resurgence of American economic growth.Information technology rests in turn on the development and deployment of semiconductors–transistors, storage devices, and microprocessors. The semiconductor and IT industries are global in scope, with an elaborate international division of labor. This poses important questions about the American growth resurgence. For example, where is the evidence of the "new economy" in other leading industrialized nations? To address this question, Jorgenson compares the recent growth performance in the G7 countries–Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. Several important participants in the IT industries, such as South Korea, Malaysia, Singapore, and Taiwan, are newly industrializing economies. What does this portend for the future economic growth of developing countries? Jorgenson analyzes past and future growth trends in China and Taiwan to arrive at a fuller understanding of economic growth in the information age.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.100
GPT teacher head0.201
Teacher spread0.101 · 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 designNot applicable
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

Citations12
Published2002
Admission routes1
Has abstractyes

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