(Adapted from MacRae and Winfield, 2016)
Analytical frameworks help us identify problems and assess the merits of potential solutions. There is no roadmap for creating food policy change and we don’t have an analytical approach to food policy change in Canada that would help us address these challenges. A significant limitation of existing Canadian food policy work is the lack of frame blending to bring more explanatory power to both current phenomena and a more desirable process of change. Consequently, we attempt to unify disparate literatures pertinent to the food policy change process in Canada to create a more cohesive approach, using four case studies of analyses already conducted to demonstrate the frame blending process.
There are change frameworks in use, but many are much narrower or broader than what helps us understand food policy change. Our focus is on policy transition which is a bit different from other broad sustainability, innovation and technology transition frameworks (see Markard et al. 2012). Smith (2007) cites Berkhout (2002:1-4): “A policy goal for sustainable production and consumption systems imply a different kind of innovative activity to that traditionally associated with a single product or new business practice”. Other change frameworks attempt to explain causality for major social events. Geels (2011) addresses some of the strengths and weaknesses of other grand change theories in the context of defending his preferred approach, the Multi-level Perspective (MLP). But there are elements of change frameworks that have utility for food policy change and we attempt to weave them into our construction.
Our approach isn’t about causality, but rather “what could be” or normative approaches, changing the system in a direction of our intention. Some other food policy transition literature touches on this, but is typically more rooted in causality than our approach (for observations on the state of the field, see Hinrichs, 2014). What kind of food policy contributes to wider food system change to create sustainability and health? How can it be implemented? What is the transition path for a co-ordinated well-planned non-reactive construction of a joined-up food policy? The normative approach is about using our understanding of “what is” to help us drive “what could be”. Compared to causal theories that typically focus on socio-cultural and economic causal forces, the normative dimension means paying more attention to the role of the state and CSOs, because direction is required from such actors (Elzen et al., 2011).
We are building here on the blending frames approach of others. Elzen et al. (2011) use the Multi-level Perspective (MLP), social movement theory and Kingdon (1995) to examine shifts in animal welfare policy in the Netherlands. Stachowiak (2013), not specific to food policy change, identifies 5 global change theories with 5 others that focus more on strategy and tactics, suggesting that advocates must blend them together to achieve their purposes. Barndt (2008) assessed numerous frames and filters useful for uncovering the international supply chain story of the tomato. Many Masters students at York’s Faculty of Environmental Studies have similarly attempted to blend frames to explore certain dimensions of Canadian food policy and programme change (as examples, see Bradley and MacRae, 2009; Louden and MacRae, 2010, Patel and MacRae, 2013; Campbell and MacRae, 2013; McCallum et al., 2014).
Click on the Frame category below if you'd like to see the details on different analytical frameworks used and referenced on the site.
Political economy, inc. regime theory, metabolic rift, counter-hegeomny, surplus accumulation, supply chain analysis
Right to food
Aboriginal ways of knowing
The cultural turn
Food system of the middle
For more .....
Short supply chains
Alternative food networks
Food literacy and critical food pedagogies
Health promoting schools
Drivers/Causes of Change (Interplays of Ideas; Institutions; Interests; Physical, Technological, Environmental and Economic factors)
Perspectives from Complexity Thinking: Path Dependence and Transitions
Community of food practice
Collaborative governance (co-governance)
Loci of decision making
Given the complexity of food systems, indicators need to be integrative, addressing multiple food system functions, outcomes and actors. Zurek et al. (2018 in press) identify 4 hierarchical layers for system performance metrics specific to each policy goal: (1) individual variables can be measured, from which (2) derived variables can be constructed, and they can be combined into (3) aggregated indicators, from which (4) performance metrics can be constructed and implemented.
Unfortunately, there are limited approaches to evaluating joined up food policy initiatives. The challenges of constructing coherent and consistent instrument mixes have yet to generate any agreed upon evaluative approaches in the literature (Daugbjerg and Sønderskov, 2012).
To be reviewed:
Levkoe and Blay Palmer, Food counts, http://canadianfoodstudies.uwaterloo.ca/index.php/cfs/article/view/277/291
Bob Gibson, Sustainability assessment
EU policy evaluation frameworks ....
Lampkin, N.; Schmid, O.; Dabbert, S.; Michelsen, J. and Zanoli, R. (eds.) (2008) Organic action plan evaluation toolbox (ORGAPET). Final output of the ORGAP research project (www.orgap.org) for the European Commission. Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, UK and Research Institute of Organic Agriculture (FiBL), Frick, CH. http://orgapet.orgap.org/
CityFoodTools project Blay-Palmer, A.; Renting, H.; Dubbeling, M. City-region food systems. A literature review.; RUAF Foundation: 2015.
Prosperi, P.; Moragues-Faus, A.; Sonnino, R.; Devereux, C. Measuring progress towards sustainable food cities: Sustainability and food security indicators - Report of the ESRC financed Project “Enhancing the Impact of Sustainable Urban Food Strategies; 2015
Department for Environment - Food and Rural Affairs (defra). Indicators for a Sustainable Food System; 2011
Gustafson, D.; Gutman, A.; Leet, W.; Drewnowski, A.; Fanzo, J.; Ingram, J. Seven Food System Metrics of Sustainable Nutrition Security. Sustainability 2016, 8, 196
FAO. SAFA Sustainability Assessment of Food and Agriculture systems - Guidelines Version 3.0; Food and Agriculture Organization of the United Nations (FAO): Rome, 2014
Schader, C.; Grenz, J.; Meier, M.S.; Stolze, M. Scope and precision of sustainability assessment approaches 682 to food systems. Ecology and Society 2014, 19, 42
Heller, M. C., Keoleian, G. A., & Willett, W. C. (2013). Toward a Life Cycle-Based, Diet-level Framework for Food Environmental Impact and Nutritional Quality Assessment: A Critical Review. Environmental Science & Technology, 47(22),
Life cycle-based sustainability indicators for assessment of the US food system
MC Heller, GA Keoleian Center for Sustainable Systems, University of Michigan
Dobell, R. (2003). The role of government and the government’s role in evaluating government: Insider information and outsider beliefs. Panel on the Role of Government in Ontario. Research Paper No. 47.
Mayne, J. (2003). Results-based governance: Collaborating for outcomes. In A. Gray, B. Jenkins, F. Leeuw, & J. Mayne (Eds.), Collaboration in public services: The challenge for evaluation (pp. 155–178. ). New Brunswick, NJ: Transaction.
Acharya, T.; Fanzo, J.; Gustafson, D.; Ingram, J. S. I.; Schneeman, B. Assessing Sustainable Nutrition Security: The Role of Food Systems; The International Life Sciences Institute, Research Foundation, Center for Integrated Modeling of Sustainable Agriculture and Nutrition: Washington, DC, 2014;
Gustafson, D.; Gutman, A.; Leet, W.; Drewnowski, A.; Fanzo, J.; Ingram, J. Seven food system metrics of sustainable nutrition security. Sustainability 2016, 8, 1–17.
Allen, T.; Prosperi, P. Metrics of Sustainable Diets and Food Systems; Workshop Report. Bioversity International & CIHEAM-IAMM. Montpellier, France, 2014;
 Geels (2011:34): “Frameworks such as the MLP are not ‘truth machines’ that automatically produce the right answers once the analyst has entered the data. Instead they are ‘heuristic devices’ that guide the analyst’s attention to relevant questions and problems. Their appropriate application requires both substantive knowledge of the empirical domain and theoretical sensitivity (and interpretive creativity) that help the analyst ‘see’ interesting patterns and mechanisms.”
New frameworks to guide solutions in specific areas