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BIOPRODUCT REGULATION: AN ANALYSIS OF GOVERNANCE COMPLEXITY

BIOPRODUCT REGULATION: AN ANALYSIS OF GOVERNANCE COMPLEXITY. Lisa F. Clark, VALGEN Postdoctoral Fellow Johnson- Shoyama Graduate School of Public Policy University of Saskatchewan, Canada Peter W.B. Phillips, VALGEN Co-Lead and Professor Johnson- Shoyama Graduate School of Public Policy

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BIOPRODUCT REGULATION: AN ANALYSIS OF GOVERNANCE COMPLEXITY

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  1. BIOPRODUCT REGULATION: AN ANALYSIS OF GOVERNANCE COMPLEXITY Lisa F. Clark, VALGEN Postdoctoral Fellow Johnson-Shoyama Graduate School of Public Policy University of Saskatchewan, Canada Peter W.B. Phillips, VALGEN Co-Lead and Professor Johnson-Shoyama Graduate School of Public Policy University of Saskatchewan, Canada

  2. ‘Science-based’ decision making • Regulatory systems for innovative technologies put a significant emphasis on scientific evidence in decision-making • These systems are assumed to produce efficient and rational outcomes because science is usually deemed to be norm and value free • Formal decision-making systems are often visually represented like this… Bioproduct Regulation: An Analysis of Governance Complexity

  3. Regulatory pathway for Plants with Novel Traits (PNTs) Sources: Compiled by VALGEN research team from Bean, 2011 and CFIA/PBO, 2008. Bioproduct Regulation: An Analysis of Governance Complexity

  4. ‘Evidence-based’ decision making • ‘Non-science’ forms of evidence are parts of the management of bioproducts and crops • Normative understandings of acceptable levels of exposure to risk (not typically captured in decision-making flow charts), have serious implications for decision-making • In reality, decision-making systems look like this… Bioproduct Regulation: An Analysis of Governance Complexity

  5. Regulatory pathway for PNTs (including ‘optional’ steps) Source: Compiled by VALGEN research team Bioproduct Regulation: An Analysis of Governance Complexity

  6. Complexity and Decomposability • Simon (1962): convoluted and often invisible exchanges/transactions between sub-system components can be revealed by decomposing the system • Revealing unseen information transactions show factors that impact behaviour of the system that may not be apparent in official decision-making procedures • Can use the idea of complexity to observe points within decision-making systems that may contribute to unanticipated outcomes Bioproduct Regulation: An Analysis of Governance Complexity

  7. Operationalizing Complexity • Social Networking Analysis (SNA) • Social power derived from interconnectedness • Actor interactions within governance frameworks • Kurtosis Analysis • Statistical test measures the distribution of a random variable in relation to the mean of a ‘Normal’ distribution curve (the ‘bell’ curve) • Identifies patterns of input and output distributions Bioproduct Regulation: An Analysis of Governance Complexity

  8. Case study: Canadian regulatory system for Plants with Novel Traits (PNTs) • SNA • policy (Directives, Acts) • regulator interviews (CFIA, PBO, AAFC) • department/agency websites • PNT Decision Documents • Kurtosis Analysis • PNT field trials (inputs) • PNT approvals (outputs) Bioproduct Regulation: An Analysis of Governance Complexity

  9. SNA Matrix of Maximum Interactions 1= unidirectional (A=sent; B= received) 2= bidirectional unclear Source: Compiled by VALGEN research team Bioproduct Regulation: An Analysis of Governance Complexity

  10. SNA of Canadian PNT approval process (max. interactions) Source: Calculated by VALGEN research team using ORA software Bioproduct Regulation: An Analysis of Governance Complexity

  11. Number of Plants with Novel Traits (PNT) Field Trials and Approvals in Canada (1988-2010) Source: Compiled by VALGEN research team Bioproduct Regulation: An Analysis of Governance Complexity

  12. Average proportion of total field trials 1988-2010 Average proportion of total approvals 1995-2010 Source: Calculated by VALGEN research team using STATA Bioproduct Regulation: An Analysis of Governance Complexity

  13. Conclusions • Beta test results indicate complexity may be present in Canadian regulatory system • SNA indicates those who don’t hold official power could still exert influence on outcomes • Kurtosis analysis indicates lack of predicted pattern in outputs • Both SNA and kurtosis analysis require detailed information of interactions and system processes to have a high degree of confidence in results • Methodology is applicable to other regulatory systems for bioproducts and crops Bioproduct Regulation: An Analysis of Governance Complexity

  14. Jaime Leonard, Johnson-Shoyama Graduate School of Public Policy, MA Candidate University of Saskatchewan NyankomoMarwa, Johnson-Shoyama Graduate School of Public Policy, PhD Candidate University of Saskatchewan Camille D. Ryan, Professional Research Associate Departments of Plant Sciences & Bioresource Policy, University of Saskatchewan Kari Doerksen, VALGEN Senior Project Manager

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