Accelerating the deployment of SMRs in Canada: The importance of intermediaries
Bibliographic record
Abstract
• Focuses on the role of intermediaries in accelerating the adoption of a ‘ready to use’ but controversial clean energy technology: small modular nuclear reactors (SMRs). • Demonstrates the role of intermediaries in creating and elaborating legitimating storylines in support of the technology. • An original “cooperative” storyline used to move SMRs onto the policy agenda gives way to competing storylines as intermediaries create variations in the narrative in support of SMR designs or applications. • Intermediaries must position themselves carefully with respect to these new developments if accelerated deployment has a chance of success. • Government actors showed strong network management during agenda setting but may need to continue to play a more prominent role if divisions among the other actors continue to emerge in the SMR network. Much of the research on technological innovation, especially in the context of sustainability transitions, has focused on the early stages of innovation. Much less work has been done on successful acceleration of technological change after pre-development and take-off. Filling this gap is important for improving the chances of successful deployment of small modular reactors (SMRs). Recent work on sustainability transitions has focused on the importance of "intermediaries". These are actors and platforms that sustain the momentum of transitions by linking actors, activities, and resources. Their role in the acceleration phase is less well understood and SMRs provide a compelling case study of the challenges. This paper uses document, web, and interview data to analyze the role of intermediaries in Canadian SMR deployment, focusing particularly on the intermediaries needed for successful social innovation; identifies gaps; and evaluates the role of public policy in supporting the development of these critical relationships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".