Bibliographic record
Abstract
We construct a model of endogenous technological change with trade (in the absence of foreign direct investment separating innovation from production) that displays multiple steady states with divergence in levels and in growth rates. This shows trade can be a force for both development and underdevelopment. Our dynamic model of trade simultaneously explains: comparative advantage, the advantages of being open for the technological leader, that lagging countries might benefit from being closed, the possibility of divergence under trade for lagging countries, and under what circumstances lagging countries can converge to development under trade, possibly overtaking the leader. The sources of divergence we consider are inherent characteristics of the process of technological change (for example as described throughout Aghion and Howitt’s work). The first is the need for absorptive capacity for innovators taking advantage of leading edge technologies. The second is the existence of innovation externalities between goods, the basis of technology spillovers and of the concept of “leading edge technology.” It follows that the more goods are engaged in R&D in any country, the more productive R&D is. We provide a historical discussion of the emergence of development and underdevelopment during the 19th Century and until 1914 that is consistent with and exemplifies the possibilities explained by the model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".