Creating innovation capabilities for improving global health: Inventing technology for neglected tropical diseases in Brazil
Bibliographic record
Abstract
Abstract Previous research on innovation capabilities in emerging economies shows knowledge networks tied to Western multinationals and national governments focused on economic growth. Less understood is the innovation capability building of emerging economies to achieve ‘good health’, an important Sustainable Development Goal. Here, we present a longitudinal study of a public research organization in an emerging economy and examine how it builds innovation capabilities for creating vaccines, drugs, and diagnostics for diseases primarily affecting the poor. We study FIOCRUZ in Brazil using archival, patent, and interview data about invention of technologies for neglected tropical diseases. We contribute novel insights into the evolution of knowledge networks, as national policy integrates innovation and health goals. We found significant diversification of local and foreign knowledge sources, and substantial creation of networks with public, private, and non-governmental organizations enabling collective invention. These R&D networks attract many multinationals to collaborate on socially driven innovation projects previously non-existent in their portfolios. The public research organization leads collaborations with multinationals and diverse partners, harnessing distributed international knowledge. Our results indicate emerging economies’ capabilities depend on elevating policies to increase health access for the poor to drive innovation and promoting local R&D to generate solutions to improve health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".