1. Introduction
The past decade has seen increasing use of “co-production” concepts in social science and humanities research on climate change. In a recent review, Bremer and Meisch (2017) describe the climate change co-production literature as a complex meeting place of several academic traditions and practices, introducing both ambiguity and creativity as scholars appropriate perspectives from neighboring disciplines. They distinguished eight ways in which scholars used the term co-production, namely 1) to study how natural and social orders coevolve (Miller 2004), 2) to study how scientific and societal institutions remake each other (Mahony 2013), 3) to facilitate interaction between science producers and users (Lemos and Morehouse 2005), 4) to facilitate extended modes of science that integrate societal knowledge and values (Lorenzoni et al. 2007), 5) to jointly produce public services with citizens (Vedeld et al. 2016), 6) to build adaptive capacity in institutions (Armitage et al. 2011), 7) to facilitate social learning (Bartels et al. 2013), and 8) to empower traditional knowledge systems (Kofinas 2002). Notwithstanding this diversity, some perspectives—in particular, the approach to producer–user interaction—have attracted more attention than others (Bremer and Meisch 2017) and have benefitted from a more sophisticated methodological discussion of how to practice and evaluate co-production (see, e.g., Meadow et al. 2015; Wall et al. 2017). We argue that there are important opportunities to advance the branch of co-production as extended scientific enquiry, with particular attention to the theoretical and methodological contributions from “post-normal science.”
Post-normal science (PNS) can be seen as part of the family of co-production perspectives (see definitions 3–8 above) that normatively prescribe deliberate collaboration between people for a common goal. PNS promotes collaboration between scientists and societal actors for organizing, conducting, appraising, and using climate research, weighing scientific and nonscientific knowledge, values, and criteria in building a high-quality understanding of climate to act on (Brace and Geoghagen 2010). As such, PNS is associated with other extended modes of science under the “sustainability science” umbrella (Turner 2010), including abutting perspectives on transdisciplinarity (Swart et al. 2014) and Mode 2 science (Ison et al. 2011). But while PNS overlaps with other theories and practices, it is also unique in some important aspects. In particular, PNS focuses on knowledge quality under uncertainty. This focus contributes to the richness of co-production scholarship and addresses shortcomings in other perspectives. However, with some exceptions (Hegger et al. 2012; Schuttenberg and Guth 2015), there are very few publications that make explicit how to co-produce post-normal climate science.
In this paper we provide an account of how we practiced climate knowledge co-production as post-normal science, by translating the principles of post-normal science into the research process. However, we do not present the findings that emerged from this process, which are too wide-ranging to report on properly here. Our experiences are from the Norwegian-led TRACKS (Transforming Climate Knowledge with and for Society) project, seeking to co-produce knowledge of climate variability and impacts with communities in Sylhet Division in northeast Bangladesh. Sylhet Division experiences particularly heavy premonsoon rainfall, which is unique in Bangladesh and not well represented in scientific studies and models (Stiller-Reeve et al. 2015). This uncertainty opens a space for local and traditional knowledge to complement the existing science as a basis for decision-making (Haque et al. 2017). Indeed, facing the prospect of long-term climate change, Sylhet communities are being forced to revisit their cultural representations of climate and to reappraise their knowledge of this new climate and its impacts, particularly on agriculture (Blanchard and Bremer 2015; Bremer 2017).
2. Co-production as post-normal science
In this study, we focus on how we can implement the framework of post-normal science in a process of climate knowledge co-production. PNS scholarship has developed over more than 25 years (Funtowicz and Ravetz 1990, 1993, 1994) and remains a current framework for understanding and intervening in complex and contentious issues (Dankel Vaage and van der Sluijs 2017; Strand 2017), including climate change [see, e.g., the 2012 special issue of Nature and Culture (Vol. 7, issue 2)]. PNS is often employed as an interpretive lens for diagnosing the problems of appealing to Kuhnian “normal science” (Kuhn 1962), in which 1) knowledge involves inherent uncertainties, 2) values are in debate, 3) stakes are high, and 4) decisions are urgent (Funtowicz and Ravetz 1993). Importantly, the PNS perspective highlights how significant uncertainty undermines decisions based on scientific “truth,” demanding alternative scientific norms of “quality” for climate decisions. From this point of departure, other branches of PNS go on to suggest a conceptual approach for co-producing high-quality science under uncertainty (Turnpenny et al. 2011), presenting PNS as an extended mode of scientific practice. But some have criticized a historic lack of detailed publications on “how exactly PNS can be done” (Wesselink and Hoppe 2011, p. 394; see also Tacconi 1998), or “detailed criteria for assessing the degree of ‘post-normality’ of activities and practices” (Turnpenny et al. 2009, p. 350). And although certain authors have distilled guiding principles and practices of PNS as extended scientific practice (see, e.g., Bremer 2014; Petersen et al. 2011; Lister 1998), there is a recognized need for more concrete methodological work in this branch.
This paper reports on how we used a PNS approach for co-producing climate knowledge in TRACKS. Our practices were guided by the PNS literature, from authors who have operationalized PNS in lists of principles or criteria (Bremer 2014; Petersen et al. 2011; Lister 1998) and other descriptions of PNS approaches that are less formalized in lists of principles, and from the foundational papers of Funtowicz and Ravetz (1990, 1993), to more recent accounts such as that of Strand (2017). But rather than adopt previous lists of PNS principles we formulated our own eight principles, derived from the literature but adapted to our study to be appropriate to the scientific and social context of the Bangladeshi communities in the project (see Table 1). This is internally consistent with the core mission of PNS extended science, which ought to be fit for context and function (see below). Moreover, consortium members (and others) were well placed for distilling these principles, having written widely on PNS for various environmental issues, including climate (see, e.g., Blanchard et al. 2014; Bremer 2013, 2014, 2017; Kaiser and Stead 2002).
Principles of post-normal science (listed in italics). Adapted from the literature operationalizing extended modes of post-normal science, from work distilling principles or criteria of PNS (see Bremer 2014; Petersen et al. 2011; Lister 1998), to broader descriptions (see Funtowicz and Ravetz 1990, 1993, 1994; Strand 2017).
PNS can be seen as an approach to structuring interaction at the science–policy interface (Bremer 2013), where it arguably enters by the “science door” (Wesselink and Hoppe 2011), concerned with how better to do science for policy. Traditionally, the quality of scientific knowledge is controlled by disciplinary peer review. In PNS, knowledge quality is collaboratively produced and assessed through an extended peer review, inclusive of scientists, professionals, and other knowledge-holders affected by or with special knowledge of an issue (Funtowicz and Ravetz 1993; Turnpenny et al. 2011). These people make up the so-called extended peer community, who deliberatively evaluate different knowledge perspectives and their “fitness for the function” for addressing the issue. As equal coinvestigators, all peers have a dual responsibility to contribute their evidence of how to understand and respond to the issue, and to critically and reflexively appraise all tabled evidence (Funtowicz and Ravetz 1993, 1994; Ravetz 1999; Frame and Brown 2008). This peer work can also extend to conducting joint research, or ground testing science through fieldwork (Bremer and Funtowicz 2015). The emphasis on knowledge quality distinguishes PNS from other participatory approaches. It means establishing respectful dialogue within the extended peer community, acknowledging that each perspective is unique and important, but will be critically scrutinized (Frame and Brown 2008; Strand 2017). In this, it recognizes the inseparable nature of facts and values and seeks to include an appreciation for values in knowledge deliberations (O’Connor 1999). It also means building motivation among these peers, by co-producing knowledge that is legitimate and useful for decision-making (Ravetz 1999; van der Sluijs 2002). Importantly, the members of the extended peer community need to recognize the uncertainties surrounding knowledge of complex issues, demanding that they work together to design the process of assuring and evaluating knowledge quality (Funtowicz and Ravetz 1993; Ravetz 1999). The resulting collaborations can see groups of peers explicitly developing their own quality criteria (Petersen et al. 2011), but is more likely seen in the implicit criteria that emerge through deliberation (see, e.g., Clark and Majone 1985; Funtowicz and Ravetz 1993).
PNS has drawn certain critique (Turnpenny et al. 2011), including by its founders who lament poor uptake of extended modes of science (Ravetz 2006). Some science and technology studies (STS) scholars (e.g., Weingart 1997, p. 592) argue that approaches such as PNS “pertain to a specific section of the research system but cannot be generalized to science as a whole … there is no fundamental change in epistemology.” And to the extent that science for policy has always shaped and been shaped by politics, it has never been “normal” in that sphere. Some policy science scholars (Wesselink and Hoppe 2011) argue that PNS’s focus on “getting the science right” affords weak attention to the policy side of the science–policy interface, that is, the complexities of political deliberation and democratic interaction. They accuse PNS of scientific hubris, implicitly assuming that changing the scientific input will have the power to change policy outcomes, when for many policy thinkers science has a limited role in political interaction (Wesselink and Hoppe 2011). Going further, looking at the institutions shaping political interaction, Turnpenny et al. (2009) argue that PNS’s aspirations are difficult to realize as long as institutional rules and norms valorize so-called normal science. Within these institutions, it remains difficult to credit other tacit knowledge systems—local, traditional or craft knowledge—with quality for decision-making. Indeed, for some critics, moves to extend science are an insidious attempt to consolidate science’s privileged role in decision-making by subsuming nonscientific knowledge into a scientific framework (Turnpenny et al. 2011; Wesselink and Hoppe 2011).
These critiques raise issues of power that vex all work appealing to knowledge production based in “negotiation in good faith” (Ravetz 2006, p. 278), and which could arguably be assuaged with closer attention to PNS methodology (Tacconi 1998), a need addressed in this paper. Conceptually, PNS takes contentious and high-stakes issues as a starting condition, and attempts to mitigate adverse power dynamics, including by 1) appealing to the uncertainty that limits any actor appealing to the truth, and thus 2) opening peer status to all different stakeholders and 3) opening all knowledge claims to equal challenge by 4) using agreed-upon knowledge quality criteria (see, e.g., O’Connor 1999). However, PNS practice remains largely a mode of scientific practice, with arguably less attention to accommodating power in the study process, or how to invest resultant knowledge with decision-making power. The role of power, particularly in an age of “post-truth” and “fake news,” was a recurrent theme in the recent Third Post-Normal Science Symposium (PNS3) in Tubingen, Germany, in October 2017. Power was also an issue we faced in our research design (see section 3a).
What distinguishes PNS from other co-production approaches, and as such, what are its particular contributions to co-production theory and practice? PNS’s sophisticated attention to the science side of the science–policy interface makes it an important complement to other more politically or policy-oriented co-production perspectives. Whereas other co-production traditions look at how science is rendered politically useful for policy actors (Lemos and Morehouse 2005), integrated with institutions (Armitage et al. 2011), or challenged by traditional communities (Kofinas 2002), PNS helps us to reflect on ways to fundamentally reform scientific enquiry for policy under uncertainty, according to new norms of evidence, quality, and discourse (Funtowicz and Ravetz 1990). PNS thus opens a fruitful discussion on the quality of co-produced knowledge that goes beyond scientific criteria or usefulness alone, to encompass all criteria deemed legitimate in an institutional or wider social context. So conceived, quality can be discussed in rational, practical, or ethical dimensions (Clark and Majone 1985), with some scholars attempting to distil general quality criteria, such as knowledge salience, credibility, and legitimacy (Cash et al. 2003). This substantive focus on knowledge quality addresses a gap in the co-production literature, which focuses more often on democratic or instrumental rationales for collaboration. However, given that participation in PNS is explicitly about enhancing quality control rather than extending democracy (Yearley 2000), this can also be a source of tension with other variants of co-production, such as in STS for instance, which looks at the mutual evolution of knowledge production and social orders toward exposing and challenging dominant scientific narratives (Jasanoff and Wynne 1998; Lövbrand 2011).
3. Co-producing post-normal climate knowledge in workshops
In March 2016, the TRACKS project ran two workshops, one each in the Sunamganj Sadar and Barlekha districts of Sylhet Division. The aim was to bring together extended peer communities to co-produce knowledge about local rainfall and its impacts, and to design related indicators for their own citizen science research. By way of illustration, the workshop in Sunamganj Sadar was held over two full days, and convened 23 extended peers (7 women, 16 men), whom we now refer to as participants. The participants were divided into four groups facilitated by Bangladeshi TRACKS researchers. We arranged the groups to reflect the diverse perspectives of gender, age, background, and education. The groups included farmers, housewives, local politicians, meteorologists, teachers, journalists, pharmacists, and taxi drivers, for instance. We particularly encouraged women to attend the workshop, and facilitated their participation.
TRACKS scientific expertise was incorporated into deliberations both through the social scientists facilitating the group work, and the direct participation of climate scientists as participants in the groups. Because the discussions were in Bangla, the three Norwegian scientists present observed, took notes, and filmed parts of the workshops. It is important to recognize the positionality of project scientists in this work. Despite its creative methods—using local film, poems, and art—the workshop explicitly remained part of a global research project, convened, hosted, and facilitated by scientists. This created a unique setting for interaction that likely diminished certain local hierarchies (i.e., between men and women), while strengthening others, consolidating the position of scientists and professionals who understand scientific institutions. This in turn likely influenced the knowledge participants assumed were credible or legitimate in that setting, reflecting a key criticism of PNS as overly “scientistic.” We were upfront about this positionality, both with ourselves and with participants. We made it clear to the peer community that this was a research project, that it was unlike the nongovernmental organization (NGO)-led development projects they may have been used to, and that the discussion would be supported by conceptual tools such as cognitive maps and revolve around knowledge mobilization more than actions. At the same time, we were ourselves clear that we sought to ground the science in this local context, and thus while we as scientists could contribute to the discussion we were not to drive it. Facilitators and climate scientists alike let other participants talk first, before complementing the discussion with their opinions. For example, the research framing—on summer rainfall and impacts—came directly from community participants through a series of interviews preceding the workshop.
We designed this process according to the principles of post-normal science, which are highlighted in italics as we now describe each step of the workshops.
a. Preworkshop activities
The workshops were an important step in a longer research process that had already established an understanding of the local context of climate knowledge and mobilized and motivated stakeholders to form extended peer communities. Thus while we focus on the workshops, these events must be seen in the context of the research process that started before, and continued after as a “citizen science” effort.
TRACKS began in September 2014 with kickoff meetings in Dhaka and Sylhet. These meetings brought together national and local level experts to produce a first broad-brush understanding of climate vulnerability in Sylhet Division. This identified key local weather phenomena, their impacts on particularly vulnerable communities, and important local stakeholders.
Based on this information, we then conducted 234 narrative interviews within communities in two districts of Sylhet Division (Bremer et al. 2017). The interviews were designed to elicit stories about local weather to reveal stakeholders’ tacit knowledge embedded in their cultural representations, values, and meanings. The stories revealed different ways of knowing the weather in these local contexts, and what is not known. The results also steered TRACKS’ research toward premonsoon rainfall as an important phenomenon. Finally, the interviews were a first step in establishing a relationship with the extended peer community, and helped us identify possible participants for our workshops and citizen science research. We asked interviewees directly after each interview whether they wanted to continue to be a part of the project based on their initial interaction with us. From this short list we sought a group that represented, as closely as possible, the diverse stakeholder categories mapped at the beginning of the project (Bremer et al. 2017). Participants were also selected based on their openness, as determined by the length of their transcribed interview, and their understanding, as determined by the sophistication of their responses. In the end we were left with 54 knowledgeable and enthusiastic stakeholders to participate across both workshops. The objective of the workshops was then to increase this motivation so that the participants would continue to collaborate after the workshops were over.
We worked particularly carefully toward gender balance at the workshops, starting from recruiting participants for interviews. This was a challenge in the rural northeast of Bangladesh, where cultural norms can limit women’s participation in certain settings in the public sphere. Many women still require the permission of their husbands to participate in public activities. As such, while we sought at least 30% female participation in the interviews, we finished with just under 20%. In the workshops we tried to improve female participation by inviting more women and creating a workshop setting that “bracketed” some of the hierarchies active in the community. Women represented 12 out of 54 participants across the two workshops, including both women in powerful (local politicians) and weaker positions (day laborer) in the community. In the workshops we tried to group female participants in pairs at mixed tables, and facilitators consciously ensured that women were given time to air their views. Female facilitators played an important role in this regard.
Gender was one of a number of power imbalances that we had to take into practical account in workshop planning. As noted, while PNS theory accounts for power in knowledge production, PNS practice is less advanced. We sought to bracket power through 1) acquired knowledge of the peer community with attention to conflicts and hierarchies in forming groups, 2) experienced, context-appropriate facilitation with equal treatment to all participants, and 3) a concrete study design that was accessible to people of different education, that all could relate to, and that had pragmatic and political meaning for them, with an understanding people could act on or argue for.
The workshops (Table 2) were designed over six months of interdisciplinary collaboration between the TRACKS researchers. This process culminated in a one-week meeting in Dhaka in February 2016, where we refined and tested the workshop activities. The meeting provided a practical and interdisciplinary check of the method and ensured that all involved understood the principles of post-normal science steering the workshops (see Table 3).
Outline of the workshops.
A list of workshop activities and how they related to the principles of post-normal science.
b. Stimulating reflexive discussions with films
The workshops began with short, professionally made, documentary-style films (see https://vimeo.com/user33110597) that retold some narratives from the previous interviews. We used film since it is an accessible medium for participants who are illiterate. Each film told weather-related stories from a certain social group—a rice farmer, a secondary school teacher and student, and a local meteorologist—and introduced a theme to stimulate group discussion. The three discussion themes were what weather information we use, why we trust different weather information, and how we share weather information.
The films were meant to act as “icebreakers” for bridging communication boundaries across the participants in each group, and to set the scene for respectful and open dialogue. The films helped participants recognize the plural representations, knowledge, and meanings of weather in their community, accepting that all were legitimate but also subject to critical scrutiny.
Indeed, the films aimed to invest discussions with a critical and reflexive attitude. All participants were encouraged to appraise the knowledge offered by others and themselves. We chose the three discussion themes in the films to help the participants realize that knowledge is intertwined with experience and value systems and each is trusted according to their own criteria of quality.
The films also sought to situate the co-production process in the local context and anchor it in the communities’ experience of rainfall. This was seen as necessary in order to design a salient and legitimate citizen science process.
Finally we intended the films to mobilize and motivate participants to be active throughout the workshop discussions. By recalling the interviews, the films linked the workshops with the ongoing collaboration between TRACKS scientists and the extended peer community. We hoped this would reflect our genuine interest in a long-term co-production process.
c. Building fuzzy cognitive maps
In the next session we invited each group to draw fuzzy cognitive maps of the presumed causes and impacts of local rainfall. Fuzzy cognitive maps (Ozesmi and Ozesmi 2004) are qualitative models that can be constructed in groups to represent complex local systems, including climate (see, e.g., Reckien 2014; Singh and Nair 2014). Groups write key variables on a page and draw causal relationships between them with arrows. The maps are designed to bridge different knowledge perspectives by mapping different kinds of variables together: from physical quantities such as rainfall amounts to complex aggregates such as storm damage and abstract ideas such as aesthetics. In our workshops, the groups mainly focused on premonsoon rainfall, working back to look at causes and forward to look at impacts.
The maps were to help integrate participants’ perspectives into a common understanding of premonsoon rainfall and its impacts. Different perspectives could coexist on the same map, with the resulting maps including both quantitative (temperature, river levels) and qualitative parameters (feeling hot, crop abundance). The mapping exercise offered a space for reflexive dialogue that bridged boundaries between participants’ knowledge systems. In adding perspectives to the map, participants were encouraged to scrutinize their own and others’ knowledge claims; each claim had to be justified as important and believed as legitimate by the group, before it was added to the map. The justification could equally appeal to empirical evidence, observations, values, cultural meanings, or pragmatic claims of usefulness. The groups ensured knowledge quality through a carefully facilitated dialogue that negotiated trustworthiness, usefulness, and uncertainties of each perspective.
Finally, we wanted the mapping exercise to highlight uncertainties surrounding premonsoon rainfall. Uncertainties emerged in the discussion when causal relationships were difficult to explain and justify. These uncertainties related to gaps in knowledge, incompatibility of knowledge systems, or the complexity of the weather system itself.
d. Identifying areas for further research and crafting indicators
Having mapped the causes and impacts of local premonsoon rainfall, groups were asked to identify areas for further research. They listed the uncertainties they had discussed and areas where knowledge was needed to support local adaptation. The participants then collectively voted on the 10 most important areas. These areas were divided across the groups, who then designed relevant indicators and measuring methods. The indicators ranged from rainfall to storm damage (compound indicator) to mango-bud density in spring (traditional indicator). In this way, the extended peer community created a portfolio of 10 indicators of rainfall and impacts (see Table 4) that they are presently measuring as citizen scientists.
10 indicators of rainfall and impacts measured by the extended peer community, as citizen scientists.
The indicators fit the unique context of the Sylhet region. Participants were asked to design indicators that are relevant to them and their local experience. These contextualized indicators are intended to guide adaptation tailored to the local climate, economy, culture, and infrastructure. The indicators thus convey a more holistic and integrated view of the local system of rainfall and impacts.
Drafting indicators also acted as another layer of knowledge quality assessment in the face of recognized uncertainty. Participants had to justify what knowledge they lacked, what knowledge would remain uncertain, and what knowledge they needed for adaptation. In this way, indicators were assessed on their fitness to this purpose rather than revealing the “truth” about rainfall. For instance, the participants discussed the validity of indicators based on traditional weather signs, while adopting some of them into their list (such as mango buds or frogs croaking).
Finally, the indicators acted as a concrete artifact to motivate participants for the next step of the project: the measurement and logging of observations by participants as citizen scientists over two years. It was important that the workshops nurtured a sense of ownership and pride over the list of indicators that the participants had crafted.
e. Artistic representations of weather
Up to this point, the workshop activities helped participants to develop their own citizen science process. The final session aimed to further encourage communication across boundaries and motivate the participants for long-term collaboration. Notably, we invited local Baol singers (poets) to be participants in both workshops and sing well-known poems about the local environment and culture, and employed a professional artist to lead a collaborative art session.
We invited an artist from Dhaka to create an artwork based on the participants’ stories and experiences expressed in the workshops. During the workshop, the artist led a drawing exercise where he invited the participants to draw anything they wanted about the local weather. This allowed participants to consider different ways of framing their climate stories, and revealed artistic equity across social inequity.
The artist used the participants’ drawings and their discussions to create a three-canvas painting depicting the local weather indicators, events, and impacts. A few weeks later, he traveled back to Sylhet and presented the artwork to the workshop participants at one of their regular citizen scientist meetings. They gave him feedback and he changed the painting accordingly. The communities expressed their sense of ownership of the artwork that they had co-produced with the artist (Stiller-Reeve and Naznin 2018).
4. Conclusions
We have illustrated how co-production can be an extended mode of scientific enquiry of climate through our practical experiences with the TRACKS project, working with communities in northeast Bangladesh. In particular, we have shown how we adopted PNS principles from the literature, and designed methods to give effect to these principles for co-producing climate knowledge. In doing so, we hoped to demonstrate the utility of these PNS principles for steering extended modes of research with real world application. At the same time, we hoped to show how complicated it can be to give effect to PNS concepts in highly contentious, high-stakes political arenas, when knowledge co-production can be interrupted by power imbalances. The paper’s simple ambition is to add to a still-limited corpus of accounts of how to “do PNS” as a research method, both in the PNS literature, and the wider scholarship on co-production as extended science, particularly in climate change research.
What is the significance of the northeast Bangladesh case, and how generalizable is the PNS method applied there? On one hand, the climate impacts studied were highly particular to the climate phenomena and vulnerabilities and uncertainties in that place, necessitating a PNS approach in the first place. By their nature, PNS practices are highly contingent, with their methods tailored to the local issue and peer community. PNS approaches are far from limited to the methods we presented here; there is a sizeable literature on PNS tools (see e.g., Petersen et al. 2011; Strand 2017). But with consideration for the Bangladeshi context, and the communities we were working with, we purposefully chose to employ kickoff meetings, narrative interviews, the interdisciplinary workshop design, film sessions, fuzzy cognitive maps, indicator design, and art collaborations, which all culminated in citizen science research. On the other hand, many of the characteristics of the research challenge—the particularity of climate phenomena, significant uncertainty, high levels of vulnerability, cultural specificity, contingent knowledge systems, and urgency—make the case somewhat typical of many climate vulnerable communities in developing countries. Some of the activities may be inappropriate or need adjusting in other contexts, but the PNS principles and the research design process are likely applicable to many different contexts.
The next step is to empirically substantiate to what extent PNS principles and methods are actually successful in co-producing climate knowledge of higher quality for adaptation governance than might be produced otherwise (e.g., through “normal” applied science) (Weingart 1997). Although there is increasing work in this direction (Bremer 2014; Petersen et al. 2011; Strand 2017), the need for more substantiation is an important challenge for the field. PNS could benefit from emulating the organized empirical evaluation seen in research about co-production as interactive science (see, e.g., Wall et al. 2017).
This paper has more modest goals and does not have the space here to expand in depth on evaluation, which is reported on in detail elsewhere (Kvamme 2017). But TRACKS did incorporate self-evaluation by the peer community of how the process, workshops included, impacted them relative to their adaptive capacity. Evaluation followed an “adaptive governance” framework, with peers themselves putting forward criteria that were categorized relative to how the project contributed to “human, social, technological, political, and institutional capital” in the peer community. A subset of 23 peers were subsequently interviewed relative to these criteria during the citizen science work over the year following the workshops. In short, the evaluation found important increases in human and social capital and modest increases in technological capital, but relatively low increases in political and institutional capital. This may tentatively show that our PNS approach had a weak impact on local political decision-making power and suggest the need for more attention to power in PNS practice.
While self-appraisal is internally consistent with a PNS approach, there is also a need for more objective measures. While such measures are categorically difficult in such co-production processes, one indication could be the degree of collaboration, and whether it has continued. We can report that 24 months after the workshops, and with the TRACKS project finished, many participants of the extended peer communities continue to meet every two months, log their data on a dedicated website (http://projecttracks.net/lab), and discuss their new knowledge and its quality. The ongoing, voluntary participation within these peer communities—from both women and men, across different societal positions—as well as the mobilization of resources and networks that sustain this work is a tentative indication of the workshop’s success for integrating post-normal climate science into local communities.
Acknowledgments
The authors thank our TRACKS project colleagues who helped plan and carry out these workshops, and had such an important role in creating an open learning atmosphere in partnership with the Sylhet communities. We want to equally thank all of the members of the extended peer communities, who have shown such enthusiasm and interest in working with us. Finally, this research was made possible by funding of the Research Council of Norway under the KLIMAFORSK programme.
REFERENCES
Armitage, D., F. Berkes, A. Dale, E. Kocho-Schellenberg, and E. Patton, 2011: Co-management and the co-production of knowledge: Learning to adapt in Canada’s Arctic. Global Environ. Change, 21, 995–1004, https://doi.org/10.1016/j.gloenvcha.2011.04.006.
Bartels, W. L., and Coauthors, 2013: Warming up to climate change: A participatory approach to engaging with agricultural stakeholders in the Southeast US. Reg. Environ. Change, 13 (Suppl. 1), 45–55, https://doi.org/10.1007/s10113-012-0371-9.
Blanchard, A., and S. Bremer, 2015: Climate change and agri-cultural knowledge: Bangladesh through a mirror and a magnifying glass. Know Your Food: Food Ethics and Innovation, D. E. Dumitras, I. M. Jitea, and S. Aerts, Eds., Wageningen Academic Publishers, 387–393, https://doi.org/10.3920/978-90-8686-813-1_58.
Blanchard, A., and Coauthors, 2014: Harmful routines? Uncertainty in science and conflicting views on routine petroleum operations in Norway. Mar. Policy, 43, 313–320, https://doi.org/10.1016/j.marpol.2013.07.001.
Brace, C., and H. Geoghegan, 2010: Human geographies of climate change: Landscape, temporality, and lay knowledges. Prog. Hum. Geogr., 35, 284–302, https://doi.org/10.1177/0309132510376259.
Bremer, S., 2013: Mobilising high quality knowledge through dialogic environmental governance: A comparison of approaches and their institutional settings. Int. J. Sustain. Dev., 16, 66–90, https://doi.org/10.1504/IJSD.2013.053791.
Bremer, S., 2014: ‘No right to rubbish’: Mobilising post-normal science for planning Gisborne’s wastewater outfall. Mar. Policy, 46, 22–30, https://doi.org/10.1016/j.marpol.2014.01.001.
Bremer, S., 2017: Have we given up too much? On yielding climate representation to experts. Futures, 91, 72–75, https://doi.org/10.1016/j.futures.2017.01.008.
Bremer, S., and S. Funtowicz, 2015: Negotiating a place for sustainability science: Narratives from the Waikaraka Estuary in New Zealand. Environ. Sci. Policy, 53, 47–59, https://doi.org/10.1016/j.envsci.2014.11.006.
Bremer, S., and S. Meisch, 2017: Co-production in climate change research: Reviewing different perspectives. Wiley Interdiscip. Rev.: Climate Change, 8, e482, https://doi.org/10.1002/wcc.482.
Bremer, S., A. N. M. Blanchard, M. A. Stiller-Reeve, M. M. Haque, and E. Tvinnereim, 2017: Narrative as a method for eliciting tacit knowledge of climate variability in Bangladesh. Wea. Climate Soc., 9, 669–686, https://doi.org/10.1175/WCAS-D-17-0007.1.
Cash, D. W., W. C. Clark, F. Alcock, N. M. Dickson, N. Eckley, D. H. Guston, J. Jäger, and R. B. Mitchell, 2003: Knowledge systems for sustainable development. Proc. Natl. Acad. Sci. USA, 100, 8086–8091, https://doi.org/10.1073/pnas.1231332100.
Clark, W. C., and G. Majone, 1985: The critical appraisal of scientific inquiries with policy implications. Sci. Technol. Hum. Values, 10, 6–19, https://doi.org/10.1177/016224398501000302.
Dankel, D. J., N. S. Vaage, and J. P. van der Sluijs, 2017: Post-normal science in practice. Futures, 91, 1–4, https://doi.org/10.1016/j.futures.2017.05.009.
Frame, B., and J. Brown, 2008: Developing post-normal technologies for sustainability. Ecol. Econ., 65, 225–241, https://doi.org/10.1016/j.ecolecon.2007.11.010.
Funtowicz, S., and J. Ravetz, 1990: Uncertainty and Quality in Science for Policy. 1st ed. Kluwer Academic Publishers, 231 pp.
Funtowicz, S., and J. Ravetz, 1993: Science for the post-normal age. Futures, 25, 739–755, https://doi.org/10.1016/0016-3287(93)90022-L.
Funtowicz, S., and J. Ravetz, 1994: The worth of a songbird: Ecological economics as a post-normal science. Ecol. Econ., 10, 197–207, https://doi.org/10.1016/0921-8009(94)90108-2.
Haque, M. M., S. Bremer, S. Bin Aziz, and J. P. van der Sluijs, 2017: A critical assessment of knowledge quality for climate adaptation in Sylhet Division, Bangladesh. Climate Risk Manage., 16, 43–58, https://doi.org/10.1016/j.crm.2016.12.002.
Hegger, D., M. Lamers, A. Van Zeijl-Rozema, and C. Dieperink, 2012: Conceptualising joint knowledge production in regional climate change adaptation projects: Success conditions and levers for action. Environ. Sci. Policy, 18, 52–65, https://doi.org/10.1016/j.envsci.2012.01.002.
Ison, R., and Coauthors, 2011: Sustainable catchment managing in a climate changing world: New integrative modalities for connecting policy makers, scientists and other stakeholders. Water Resour. Manage., 25, 3977–3992, https://doi.org/10.1007/s11269-011-9880-4.
Jasanoff, S., and B. Wynne, 1998: Science and decisionmaking. Human Choice and Climate Change: The Societal Framework, S. Rayner and E. L. Malone, Eds., Battelle Memorial Institute, 1–87.
Kaiser, M., and S. M. Stead, 2002: Uncertainties and values in European aquaculture: Communication, management and policy issues in times of “changing public perceptions.” Aquacult. Int., 10, 469–490, https://doi.org/10.1023/A:1023963326201.
Kofinas, G., 2002: Community contributions to ecological monitoring: Knowledge co-production in the US–Canada Arctic borderlands. The Earth is Faster Now: Indigenous Observations of Arctic Environmental Change, I. Krupnik and D. Jolly, Eds., Arctic Research Consortium, 54–91.
Kuhn, T. S., 1962: The Structure of Scientific Revolutions. University of Chicago Press, 212 pp.
Kvamme, S., 2017: Evaluating adaptive governance in northeast Bangladesh: A case study of the TRACKS Project. Master’s thesis, Department of Administration and Organisation Theory, University of Bergen, 126 pp.
Lemos, M. C., and B. J. Morehouse, 2005: The co-production of science and policy in integrated climate assessments. Global Environ. Change, 15, 57–68, https://doi.org/10.1016/j.gloenvcha.2004.09.004.
Lister, N.-M. E., 1998: A systems approach to biodiversity conservation planning. Environ. Monit. Assess., 49, 123–155, https://doi.org/10.1023/A:1005861618009.
Lorenzoni, I., M. Jones, and J. R. Turnpenny, 2007: Climate change, human genetics, and post-normality in the UK. Futures, 39, 65–82, https://doi.org/10.1016/j.futures.2006.03.005.
Lövbrand, E., 2011: Co-producing European climate science and policy: A cautionary note on the making of useful knowledge. Sci. Public Policy, 38, 225–236, https://doi.org/10.3152/030234211X12924093660516.
Mahony, M., 2013: Boundary spaces: Science, politics and the epistemic geographies of climate change in Copenhagen, 2009. Geoforum, 49, 29–39, https://doi.org/10.1016/j.geoforum.2013.05.005.
Meadow, A. M., D. B. Ferguson, Z. Guido, A. Horangic, G. Owen, and T. Wall, 2015: Moving toward the deliberate co-production of climate science knowledge. Wea. Climate Soc., 7, 179–191, https://doi.org/10.1175/WCAS-D-14-00050.1.
Miller, C. A., 2004: Climate science and the making of a global political order. States of Knowledge: The Co-Production of Science and Social Order, S. Jasanoff, Ed., Routledge, 46–66.
O’Connor, M., 1999: Dialogue and debate in a post-normal practice of science: A reflexion. Futures, 31, 671–687, https://doi.org/10.1016/S0016-3287(99)00026-9.
Ozesmi, U., and S. L. Ozesmi, 2004: Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecol. Modell., 176, 43–64, https://doi.org/10.1016/j.ecolmodel.2003.10.027.
Petersen, A. C., A. Cath, M. Hage, E. Kunseler, and J. P. van der Sluijs, 2011: Post-normal science in practice at the Netherlands Environmental Assessment Agency. Sci. Technol. Hum. Values, 36, 362–388, https://doi.org/10.1177/0162243910385797.
Ravetz, J., 1999: What is post-normal science? Futures, 31, 647–654, https://doi.org/10.1016/S0016-3287(99)00024-5.
Ravetz, J., 2006: Post-normal science and the complexities of the transitions to sustainability. Ecol. Complex., 3, 275–284, https://doi.org/10.1016/j.ecocom.2007.02.001.
Reckien, D., 2014: Weather extremes and street life in India—Implications of fuzzy cognitive mapping as a new tool for semi-quantitative impact assessment and ranking of adaptation measures. Global Environ. Change, 26, 1–13, https://doi.org/10.1016/j.gloenvcha.2014.03.005.
Schuttenberg, H. Z., and H. K. Guth, 2015: Seeking our shared wisdom: A framework for understanding knowledge co-production and coproductive capacities. Ecol. Soc., 20, 15, https://doi.org/10.5751/ES-07038-200115.
Singh, P. K., and A. Nair, 2014: Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach. Climatic Change, 127, 475–491, https://doi.org/10.1007/s10584-014-1275-0.
Stiller-Reeve, M., and Z. Naznin, 2018: A climate for art: Enhancing scientist–citizen collaboration in Bangladesh. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-16-0044.1, in press.
Stiller-Reeve, M., M. A. Syed, T. Spengler, J. A. Spinney, and R. Hossain, 2015: Complementing scientific monsoon definitions with social perception in Bangladesh. Bull. Amer. Meteor. Soc., 96, 49–57, https://doi.org/10.1175/BAMS-D-13-00144.1.
Strand, R., 2017: Post normal science. Routledge Handbook of Ecological Economics: Nature and Society, C. L. Spash, Ed., Routledge, 288–297.
Swart, R., R. Biesbroek, and T. C. Lourenco, 2014: Science of adaptation to climate change and science for adaptation. Front. Environ. Sci., 2 (29), https://doi.org/10.3389/fenvs.2014.00029.
Tacconi, L., 1998: Scientific methodology for ecological economics. Ecol. Econ., 27, 91–105, https://doi.org/10.1016/S0921-8009(97)00134-1.
Turner, B. L., II, 2010: Vulnerability and resilience: Coalescing or paralleling approaches for sustainability science? Global Environ. Change, 20, 570–576, https://doi.org/10.1016/j.gloenvcha.2010.07.003.
Turnpenny, J., I. Lorenzoni, and M. Jones, 2009: Noisy and definitely not normal: Responding to wicked issues in the environment, energy and health. Environ. Sci. Policy, 12, 347–358, https://doi.org/10.1016/j.envsci.2009.01.004.
Turnpenny, J., M. Jones, and I. Lorenzoni, 2011: Where now for post-normal science?: A critical review of its development, definitions, and uses. Sci. Technol. Hum. Values, 36, 287–306, https://doi.org/10.1177/0162243910385789.
van der Sluijs, J. P., 2002: A way out of the credibility crisis of models used in integrated environmental assessment. Futures, 34, 133–146, https://doi.org/10.1016/S0016-3287(01)00051-9.
Vedeld, T., A. Coly, N. M. Ndour, and S. Hellevik, 2016: Climate adaptation at what scale? Multi-level governance, resilience, and co-production in Saint Louis, Senegal. Nat. Hazards, 82 (Suppl. 2), 173–199, https://doi.org/10.1007/s11069-015-1875-7.
Wall, T., A. M. Meadow, and A. Horganic, 2017: Developing evaluation indicators to improve the process of co-producing usable climate science. Wea. Climate Soc., 9, 95–107, https://doi.org/10.1175/WCAS-D-16-0008.1.
Weingart, P., 1997: From “finalization” to “mode 2”: Old wine in new bottles? Soc. Sci. Inf., 36, 591–613, https://doi.org/10.1177/053901897036004002.
Wesselink, A., and R. Hoppe, 2011: If post-normal science is the solution, what is the problem?: The politics of activist environmental science. Sci. Technol. Hum. Values, 36, 389–412, https://doi.org/10.1177/0162243910385786.
Yearley, S., 2000: Making systematic sense of public discontents with expert knowledge: Two analytical approaches and a case study. Public Understanding Sci., 9, 105–122, https://doi.org/10.1088/0963-6625/9/2/302.
