what do government and nonprofit stakeholders want to know about nuclear fuel cycles? a semantic...
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What Do Government And Nonprofit Stakeholders Want To Know About Nuclear Fuel Cycle? A Semantic Network Analysis
Approach
Nan LiDominique Brossard*
Dietram ScheufeleDepartment of Life Sciences Communication
College of Agricultural and Life SciencesUniversity of Wisconsin – Madison, USA
SRA Annual Meeting, Baltimore, MD December 8-11 2013
NUCLEAR ENERGY IS MORE AND MORE POLITICIZED
Friedrichs, J. (2011). Peak energy and climate change: The double bind of post-normal science. Futures, 43(4), 469–477. .
NUCLEAR FUEL CYCLES: COMPLEX RISKS AND REGULATORY UNCERTAINTY
• Cost- Capital cost, operating cost,
maintenance cost, carbon emission credits
• Waste management- Feasibility of geological disposal,
reprocessing and recycling
- Safety- Power plant safety, safety of the
overall fuel cycle
• Nuclear proliferation
MIT. (2003). The future of nuclear power (pp. 1–26). Cambridge, MA.
STAKEHOLDERS APPLY DIFFERENT MENTAL MODELS WHEN MAKING DECISIONS
Skarlatidou, A., Cheng, T., & Haklay, M. (2012). What do lay people want to know about the disposal of nuclear waste? A mental model approach to the design and development of an online risk communication. Risk Analysis, 32(9), 1496–511.
OUR PROJECT
• Identify U.S. federal agencies and nonprofit organizations as two important stakeholders involved in making policy decisions
• Understand and compare how they perceive the areas associated with the risks of nuclear fuel cycle
• Apply a semantic network analysis approach to examining the stakeholders’ mental models
METHODS
• Conduct one-hour-long cognitive interviews with 6 government and 6 nonprofit stakeholders between April and June 2012
• Ask questions about different dimensions of the risks associated with nuclear fuel cycle (e.g., economics, safety, waste, proliferation etc.)
• Use the artificial neural network software CATPAC II to analyze the transcripts (9,929 words for government and 12,130 words for nonprofit stakeholders)
• Describe the contents of mental models using Hierarchical Cluster
Analysis (HCA) and Multidimensional Scaling (MDS)
HOW THE SOFTWARE WORKS
• Read through the text with a window of n (n=5) words (e.g., 1 to 6, 2 to 7, 3 to 8) and document the co-occurring patterns of words
• Hierarchical Cluster Analysis to analyze the word covariance matrix- The clustered words represent the frequently co-occurring concepts
• Multidimensional Scaling analysis helps draw the “conceptual maps”
and visualize the contents of mental models- The grouped words represent the emergent meaning and dominant themes of
the text
HCA RESULTS: GOVERNMENT STAKEHOLDERS
Yucca Mountain
Environment, Transportation and Local Impact
Recycling
Proliferation
Economic and Waste Management
C1 C2 C3 C4 C5
MDS MAP: GOVERNMENT STAKEHOLDERS
Notes: Dark points represent the concepts that do not fit the results of hierarchical cluster analysis
Cluster 2: Proliferation
Cluster 3: Economic and waste management
Cluster 4: Recycling
Cluster 5: Environment,
transportation and local impact
HCA RESULTS: NONPROFIT STAKEHOLDERS
ProliferationAlternative energy
Economic
Yucca MountainReprocessing
Uranium and waste storage
C1 C2 C3 C4 C5 C6
MDS MAP: NONPROFIT STAKEHOLDERS
Notes: Dark points represent the concepts that do not fit the results of hierarchical cluster analysis
Cluster 1: Proliferation
Cluster 2: Alternative energy Cluster 3: Economic
Cluster 4: Yucca
Mountain
Cluster 6: Uranium and waste storage
SUMMARY
Note: Bolded words are unique to the particular group in each column.
DISCUSSION AND CONCLUSION
• Mental models vary for different stakeholders because they are closely related to institutional responsibilities and tasks
• Semantic network analysis approach is appropriate for describing
and comparing the contents of stakeholder mental models
• Should not assume the consistency in cognitions and risk-related beliefs between stakeholders with distinct expertise and interests
• Risk communication efforts are needed to promote stakeholder participation in policymaking
Thanks for your attention
[email protected]: @brossardd
This material is based on work supported by grants from the U.S. Department of Energy (Contract No. 120341). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Energy.