Collaboration for Sustainable Innovation Ecosystem: The Role of Intermediaries
Bibliographic record
Abstract
Innovation ecosystems have increasingly been studied from various perspectives, including connecting innovation ecosystems to sustainable development. Extant studies have found that innovation is important for sustainable development, collaboration is important for innovation, and intermediaries create necessary links and opportunities for the development of relations and cooperation between different actors in an ecosystem. What has been missing, however, is an explicit analysis of the process of collaboration in innovation ecosystems to ensure sustainability and the role of intermediaries in the process. To fill this void, this paper studies six organizations that act as intermediaries, using a multiple-case design approach. It analyzes the process of collaboration in innovation ecosystems and elucidates the role of intermediaries in the development of sustainable ecosystems. The findings indicate that the process of collaboration between actors in innovation ecosystems is an iterative process facilitated by intermediaries. By connecting different actors, intermediaries support the diffusion of innovation that has important implications for building sustainable innovation ecosystems and achieving Sustainable Development Goals (SDGs).
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".