How Cities Think: Knowledge Co-Production for Urban Sustainability and Resilience
2. Knowledge Co-Production for Sustainability and Resilience
3. Knowledge Systems Analysis: A Framework to Design Knowledge Co-Production in Cities
4. Design Philosophies for Knowledge Co-Production for Urban Sustainability and Resilience
4.1. Context and Inclusiveness
- Analyze existing knowledge systems; do not make assumptions about how they work in the city: What do people know or need to know about the city? Who are the key actors producing and using knowledge for urban planning and sustainability? How are their knowledge systems structured and functioning? What epistemic practices inform their visions and expectations of the city? How is their network constituted? How do the credibility and legitimacy of science and other knowledge play out in this context? What actors are perceived as credible and legitimate, and why or why not?
- Expose researchers to these conditions and the complex social–ecological realities of the place. Ethnographic research approaches, such as field work, observations and unstructured interviews can be useful tools to build epistemic context and initiate rapport, and hence trust, with local stakeholders.
- Identify all knowledge-relevant stakeholders (including marginal actors) and engage early to assess their needs, priorities, and existing knowledge systems. Develop trust by engaging in multiple ways, formally and informally, and continuously follow-up and communicate with stakeholders.
4.2. Adaptability and Reflexivity
- Institute an advisory review body, in which both political interests and epistemologies (ways of knowing) are represented, and build accountability in the knowledge production process.
- Be flexible with engagement methods—use a variety of methods with varying frequencies, including consultative (e.g., surveys, rapid appraisals), informal meetings (e.g., office visits, fields trips), and active participation (e.g., engagement in decisions on research) to develop an appropriate framework that fits local context and the diversity of ways that researchers and practitioners are able to engage given different reasoning styles, time, and other capacities.
- Iteratively frame research agenda and process; approach knowledge systems as experiments; evaluate and adapt.
- Monitor knowledge systems through learning indicators and knowledge system analysis and evaluation.
- Account for the ‘intangibles’, or non-quantifiable elements, of quality of life in a city.
4.3. Knowledge–Action Networks
- Evaluate and invest existing institutional structure and capacities for co-production: do not assume capacity is already there. Where capacities do exist, work or help transform them, instead of automatically building new structures (e.g., new organization).
- Recognize that in an increasingly networked society, power and knowledge are distributed, thus the knowledge–action networks need to be cognizant of the distribution of expertise in the governance space and the inevitable political, organizational, and operational complexity that this creates.
- Develop epistemic or transdisciplinary consortiums: instead of looking for uniformity or consensus, foster diversity and pluralism of ideas, knowledge and ways of reasoning. Individuals trusted and deemed credible by researchers and stakeholders alike can serve as the ‘mediators’ between knowledge and action.
- Create a variety of spaces and/or activities or support others in leading them (i.e., field trips, seminars, workshops, retreats, office visits, etc.) to deliberate research questions and outputs such that stakeholders feel ownership of the process.
- Develop a network imaginary as the cultural glue to keep the network together and thus allow actors to have ownership of the process and outcomes of the networked structure.
Conflicts of Interest
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|Framework Concepts||Definition or Use in Knowledge Systems Analysis||Example|
|Knowledge Claims||Statements or propositions about the world whose relationship to truth cannot be easily or directly ascertained. Whether they are correct or not is always uncertain, at least to some degree.||The statement that “the 2010 Census enumerated 308,745,538 people in the US” is a claim, since the Census cannot obtain an exact count of every single person in the country.|
|Values and Standards||Define the foundation of knowledge production in the system through a process of simplification, or creating simplified representations of complex social and/or natural processes. Which aspects of reality get simplified, and to what extent, is a value choice.||Standardized methodologies to measure greenhouse emissions defined by normative principles outlined by the Conference of Parties of the UN Framework Convention on Climate Change.|
|Epistemologies||Ways of knowing and reasoning about the world, including a diversity of elements, such as problem framing, forms of evidence and argumentation, deeper imaginaries that inform them, and the technologies used to produce knowledge.||As knowledge production methods, statistical and experimental epistemologies employ different analytical and conceptual approaches, techniques, standards of evidence, and underlying assumptions of causality.|
|Structures||The social and organizational arrangements, networks, and institutions of the people that construct knowledge. Involves understanding how the people involved in knowledge systems are organized, trained, evaluated, and rewarded for their work.||The USDA Forest Service Research and Development program is a highly-structured knowledge system, organized into many levels of research science (e.g., GS level), with specified standards and norms that define the expectations of the scientists' work and level of productivity.|
|Framework Concepts||Definition or Use in Knowledge Systems Analysis||Example|
|Generation||The process and activities of problem formulation, data collection, data analysis, and reporting of information. A common example is research, whether scientific, market, or journalistic research. Activities include the ways these activities are carried out, by whom, with what attention to detail and with what methodologies and resources.||The Census data collection process involves significant fieldwork (e.g., surveyors that travel around communities knocking on doors for people to fill out their forms), but also legal and political work that govern knowledge generation (e.g., Congress writes laws specifying how the Census will be conducted). Agencies must also develop regulatory processes to determine exactly what data to collect and which methods to use.|
|Validation||The practices, processes, and routines by which knowledge claims are subject to review, critique, assessment, or check. Includes who in a knowledge system is assessing, reviewing, testing, or otherwise checking the knowledge that is being generated.||The National Science Foundation peer review process is known for the rigor of its procedures and the caliber of the scientists that it brings together to evaluate the quality of the research generated by the agency’s funding.|
|Circulation or Communication||The practices by which knowledge claims are exchanged, transmitted, or translated from one location to another. Involves sorting out who has access to new knowledge claims, through what channels, whether those are the right people, whether the forms of communication are properly communicating enough additional information to judge a knowledge claim and its value.||Nutritional labeling in the US is an explicit effort to ensure that knowledge claims are circulated to a wide array of citizens. The standardization of food packaging labels enhances consumer decision-making by making knowledge available and easy to read at the time of purchase.|
|Application||The social and institutional practices by which knowledge is factored into decisions. This phase is often also referred to as the use, uptake, or consumption of knowledge.||Regulatory agencies, like the EPA, have internal and external processes, such as administrative hearings, to present and review relevant scientific research when constructing a new regulatory rule. The agency must decide how to put the knowledge collected and reviewed to use, typically through formal and informal conversations and deliberations, an official judgment and then formal statement by the Administrator.|
|Framework Concepts||Definition or Use in Knowledge Systems Analysis||Example|
|Organizational Complexity||When knowledge systems are in a complex decision-making landscape that involves a multiplicity of interacting actors and viewpoints, and complicated rules of procedure. Oftentimes knowledge and decision-making become tightly coupled to one another, such that integrating new knowledge into this form of closed system can be a very difficult undertaking.||Decisions involving ecosystem services typically involve trade-offs among ecosystem services and multiple stakeholders and organizations. Knowledge of the trade-offs among ecosystem services is often absent from or neglected within disconnected decision-making processes, leading to decisions that have unexpected or problematic outcomes.|
|Operational Complexity||Conditions under which highly dynamic social work is necessary to carry out the core functions of knowledge systems, involving diverse participants and organizations, and requiring careful coordination across the system’s many organizational components.||The UN Framework Convention on Climate Change coordinates across multiple experts and organizations the various tasks of emissions inventories, including defining which emissions to count and allocate to responsible parties, the standardization of those methods, and the review processes by independent experts from other countries to ensure transparency. Boundary work and orchestration are also crucial functions to ensure legitimacy and credibility across multiple institutions and forms of expertise.|
|Political Complexity||Conditions of high interconnection between knowledge production and the exercise of political power, especially in the presence of conflicts within or between organizations. In the adversarial political context of the US, in particular, the connection of science and expert advice within many facets of decision-making in the US federal government is an illustration of the political complexity of knowledge systems.||The knowledge claims underpinning EPA regulatory decisions have been widely contested by both industry groups and environmental organizations, depending on which group perceived an interest in undermining EPA credibility on any given policy issue. Further layers of organizational complexity, e.g., the presence of the EPA Science Advisory Board, often exacerbate knowledge conflicts rather than mitigate them by presenting another opportunity for divergent views of the proper use of scientific evidence to arise and become subject to critical commentary by policy actors.|
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Muñoz-Erickson, T.A.; Miller, C.A.; Miller, T.R. How Cities Think: Knowledge Co-Production for Urban Sustainability and Resilience. Forests 2017, 8, 203. https://doi.org/10.3390/f8060203
Muñoz-Erickson TA, Miller CA, Miller TR. How Cities Think: Knowledge Co-Production for Urban Sustainability and Resilience. Forests. 2017; 8(6):203. https://doi.org/10.3390/f8060203Chicago/Turabian Style
Muñoz-Erickson, Tischa A., Clark A. Miller, and Thaddeus R. Miller. 2017. "How Cities Think: Knowledge Co-Production for Urban Sustainability and Resilience" Forests 8, no. 6: 203. https://doi.org/10.3390/f8060203