Bibliographic record
Abstract
The explanation and causes of economic growth, the problem of convergence of per capita income among different economies, the low productivity growth in many advanced economies, and the presence of disrupting technological innovations remain at the center of the debate among economists. The present contribution analyzes the endogenous growth theory of Paul Romer and discusses its features and content through Romer’s main works on the topic. This study on Romer’s work highlights the existence and importance of increasing returns in the process of growth, the key role of knowledge, the ideas as non-rival goods, the existence of externalities, the endogeneity of technological change, and the primary role of human capital, especially in research activity. Institutions, such as property rights are important as well. The state also has a decisive role in education and the research sector. Another relevant aspect is that economic growth and technological change are closely interconnected; they cannot be separated. Romer’s theory of endogenous technological change ties the development of new ideas and economic growth to the number of people working in the knowledge sector. New ideas, being non-rival and partially excludable, are fundamental for growth since they make everyone producing physical goods and services more productive. Finally, Romer’s endogenous growth highlights the factors that provide incentives for knowledge creation; thus, his theory can also be considered a significant contribution to the theory of the knowledge-based economy.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".