From green technology development to green innovation: inducing regulatory adoption of pathogen detection technology for sustainable forestry
Bibliographic record
Abstract
Technological entrepreneurship has been widely acknowledged as a key driver of modern industrial economies, and more recently, a panacea for environmental and social problems. However, our current understanding of how green-technology ventures emerge and diffuse more sustainable innovations remains limited. We advance theory on green entrepreneurship by drawing on institutional work to refine and extend our understanding of how entrepreneurs may influence government policies and practices in their attempts to diffuse green technology. We develop a theoretical framework that combines institutional work with a search tool, the technological, commercial, organizational, and societal (TCOS) framework of innovative uncertainties, which identifies key opportunities, hurdles, and potential unintended consequences at early stages of technology development. We present a detailed case study of a potential university-based green-tech venture developing pathogen detection technology for forestry protection. Foreign pathogens spread by international trade can have major detrimental impacts on forests and the industries that rely on them. Our analysis found that green technology demonstrating technological feasibility is necessary but not sufficient; green-tech ventures must also engage in institutional work, in this case, articulating the technology’s benefits to regulators to establish legitimacy and avoid misuse that can hinder its adoption. We thus add to previous studies by emphasizing that institutional work could be a main activity for a green-tech venture, a core entrepreneurial strategy rather than an afterthought.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".