Innovation intermediaries accelerating environmental sustainability transitions
Bibliographic record
Abstract
Institutions in the United States are undergoing modifications that present direct challenges for the environment and society and may result in institutional uncertainty and instability. This article explores whether innovation intermediaries can be employed as a key component of a strategy to create a window of opportunity for green job creation, infrastructure changes, and technological innovation in response to these types of institutional modifications. Based on a systematic literature review, this article outlines a framework that combines institutional modifications with technological innovation and infrastructure development as part of an economic development strategy. Important findings are that connections between innovation intermediaries, such as incubator and accelerator centers, niche actors, such as green champions, and regime actors, such as policy entrepreneurs, show potential to contribute to a green economic development strategy but require further examination for the specific roles played by policy entrepreneurs to help create the conditions for scaling niche experiments and simultaneously disrupting the regime. The key contribution is in defining the role of sustainability-oriented innovation intermediaries at linking local, state and business actions in order to scale-up and influence green economic development in a politically feasible manner during times of institutional uncertainty and instability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".