Innovation and access to technologies for sustainable development: diagnosing weaknesses and identifying interventions in the Transnational Arena
Bibliographic record
Abstract
Sustainable development – improving human well-being across present generations without compromising the ability of future generations to meet their own needs – is a central challenge for the 21st century. Technological innovation can play an important role in moving society toward sustainable development. However, poor, marginalized, and future populations often do not fully benefit from innovation due to their lack of market or political power to influence innovation processes. As a result, current innovation systems fail to contribute as much as they might to meeting sustainable development goals. This paper focuses on how actors and institutions operating in the transnational arena can mitigate such shortfalls. To identify the most important transnational functions required to meet sustainable development needs our analysis undertook three main steps. First, we developed a framework to diagnose blockages in the global innovation system for particular technologies. This framework was built on existing theory and new empirical analysis. On the theory side, we drew from the literatures of systems dynamics; technology and sectoral innovation systems, science and technology studies, the economics of innovation, and global governance. On the empirical front, we conducted eighteen detailed case studies of technology innovation in multiple sectors relevant to sustainable development: water, energy, health, food, and manufactured goods. We use the framework to analyze our case studies in the common language of (1) technology stocks, (2) non-linear flows between stocks substantiated by specific mechanisms, and (3) characteristics of actors and socio-technical conditions (STCs) which mediate the flows between stocks . We identify blockages in the innovation system for each of the cases, diagnosing where in the innovation system flows were hindered and which specific sets of STCs and actor characteristics were associated with these blockages. Figure E.1 displays the components of our framework and how they relate.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".