Mapping the Innovation Ecosystems for the Deployment of Small Modular Reactors in Canada and Mexico: An Innovation Policy Approach Through Strategic Niche Management and Social Network Analysis.
Bibliographic record
Abstract
Small Modular Reactors (SMRs) have received considerable attention as their specific designs reduce implementation times and costs, allowing modularity to increase the installed capacity for energy generation. Although SMRs represent a reliable, affordable, and sustainable alternative to meet our growing energy demands, this technology faces deployment obstacles that may require outside interventions to speed up their adoption so that people can enjoy their societal, environmental and economic benefits. Just as a country´s best energy mix approach varies by resource availability and institutional capabilities, the actors promoting SMR adoption constitute an innovation ecosystem uniquely responsive to country-specific characteristics. This thesis uses a Strategic Niche Management (SNM) framework that proposes interventions in protected spaces to determine the optimal conditions for successful deployment and appropriate policy while consolidating a community of early adopters.\n \nThrough Social Network Analysis (SNA), this thesis compares how these SMR innovation ecosystems are formed in Canada and Mexico, highlighting structural differences between developed and developing countries. This primary framework and research method are then complemented with the Helix Model IV for a comprehensive review of the governance of SMR innovation ecosystems. Policy and network structures are assumed to have a feedback loop effect on each other and SMR deployment potential. Secondary data were collected from publicly available information and processed under the software Gephi 9.5.\n \nContrary to most research, which focuses solely on centralized actors in a network, this thesis explores the contributions of both centralized and peripheral actors to the network, so policymakers can discern where to efficiently allocate resources depending on their intervention objectives and their main focus. Results indicate that the Mexican SMR ecosystem, with its visually different network structure in all the snapshots, is more vulnerable than the Canadian ecosystem. This difference is especially apparent in the scene where five of the most centralized actors are removed from the two SMR ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".