Innovation and Technology Transfer Policies in China, India, and Brazil
Bibliographic record
Abstract
Productivity growth is a significant contributor to GDP growth, particularly to increases in per capita income. However, there is considerable ambiguity regarding how to measure the concept of technical progress, and consequently on policies that would foster productivity growth. Brazil, China, and India, three important emerging economies, are seeking to foster productivity growth through encouraging innovation and technology transfers from the more developed economies. But given the ambiguities about how to encourage innovation and technology transfers, governments in these countries adopted a plethora of policies in the hope that the combination will be effective. This ambiguity can also be seen in the much slower growth of productivity in Brazil than China, even though Brazil has scored higher on theWorld Bank’s Knowledge Assessment Methodology. A common trend is to foster closer links between universities and research institutes and commercial enterprises. Chinese policy has been most forward in this respect. While such a link was behind the government’s thinking in India, until recently the link was weak and new policies have been adopted by the government since the 1991 economic reforms to strengthen the relationship. In Brazil, the link had been very weak until a few years back when the government instituted new policies to encourage the commercialization of new technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".