North-South Trade-Related Technology Diffusion : Virtuous Growth Cycles In Latin America
Bibliographic record
Abstract
This paper examines the impact on TFP in Latin America and the Caribbean (LAC) and in other developing countries (DEV) of trade-related foreign R&D (NRD), education and governance. The measures of NRD are constructed based on industry-specific R&D in the North, North-South trade patterns, and input-output relations in the South. The main findings are: i) education and governance have a much larger direct effect on TFP in LAC than in DEV, while the opposite holds for the North's R&D; and ii) education and governance have an additional impact on TFP in R&D-intensive industries through their interaction with NRD in LAC but not in DEV. These interaction effects imply that increasing the level of any of the three policy variables - education, governance or openness - result in virtuous growth cycles. These are smallest under an increase in trade, education or governance, are stronger under an increase in two of these three policy variables, and are strongest under an increase in all three variables.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".