The role of university-firm relations to foster regional development: evidence from Brazilian Amazon
Bibliographic record
Abstract
The role of universities for the innovation process of countries or regions had been widely explored. In lagged regions becomes a reference not only for qualification and research but concentrates brains and fixes qualified people. This paper analyses innovation and especially the interaction of firms with universities and research institutes, as strategy to face the low internal investment capacity in innovation. Our focus is the ultra-peripheral region of Brazilian Amazon and it is part of a larger research project which investigates these interactions internationally. The interest in studying these interactions in Brazil are based on findings that the investments in R&D by the private sector are low, and the national (and thus regional) innovation systems are immature (Albuquerque, 1998). Data was collected based on a questionnaire applied to firms, adapted by Federal University of Minas Gerais, Brazil from the Carnegie Mellon (Cohen, 2002) and Yale Surveys (Klevorick, 1995) on firms' interaction. The sample was taken from a database of university-based research groups registered in CNPq (national agency of research funding), that declared some kind of innovative relationship with firms. Although, the interaction between universities and firms has been considered crucial for the development of innovation, we found very few interactions resulting in a low complementary role or even substitute R&D efforts of these firms. Results show that the continuous interactions between firms and university are restricted to agronomy, energy, electrical and mining engineering. And that the role of university in leading the process is not sufficient to suppress the peripheral condition of the Amazon region.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".