Convergent innovation for sustainable economic growth and affordable universal health care: innovating the way we innovate
Bibliographic record
Abstract
This paper introduces convergent innovation (CI) as a form of meta-innovation-an innovation in the way we innovate. CI integrates human and economic development outcomes, through behavioral and ecosystem transformation at scale, for sustainable prosperity and affordable universal health care within a whole-of-society paradigm. To this end, CI combines technological and social innovation (including organizational, social process, financial, and institutional), with a special focus on the most underserved populations. CI takes a modular approach that convenes around roadmaps for real world change-a portfolio of loosely coupled complementary partners from the business community, civil society, and the public sector. Roadmaps serve as collaborative platforms for focused, achievable, and time-bound projects to provide scalable, sustainable, and resilient solutions to complex challenges, with benefits both to participating partners and to society. In this paper, we first briefly review the literature on technological innovation that sets the foundations of CI and motivates its feasibility. We then describe CI, its building blocks, and enabling conditions for deployment and scaling up, illustrating its operational forms through examples of existing CI-sensitive innovation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".