'Innovation policy is a team sport' - insights from non-governmental intermediaries in Canadian innovation ecosystem
Bibliographic record
Abstract
Policy-makers and practitioners alike have increasingly embraced the innovation ecosystem approach to support the flow of knowledge within the Triple Helix framework. This approach focuses on the collaborative and interdependent nature of innovation, which is based on social aspects of knowledge transfer supporting relationships, partnerships, and connections. The important role of intermediary stakeholders that help to facilitate such partnerships is under-researched. This paper examines the work of three intermediary stakeholders in the Canadian innovation ecosystem-the Canadian Science Policy Centre, the MaRS Discovery District, and university Vice Presidents Research. By interviewing 40 experts from the federal and provincial governments, non-governmental organizations, industry, and the higher education sector in Ontario, this study examines how innovation ecosystems are created and what factors influence the success of bringing diverse stakeholders together. The findings suggest that strong political vision and leadership, an inclusive approach to recognizing the needs of diverse stakeholders, and clarity on ways to measure and fund innovation serve as important factors in the Canadian innovation ecosystem.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".