Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.202 | 0.090 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".