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When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.

Mental models are personal, internal representations of external reality that people use to interact with the world around them. They are constructed by individuals based on their unique life experiences, perceptions, and understandings of the world. Mental models are used to reason and make decisions and can be the basis of individual behaviors. They provide the mechanism through which new information is filtered and stored. Recognizing and dealing with the plurality of stakeholder’s perceptions, values, and goals is currently considered a key aspect of effective natural resource management (NRM) practice. Therefore, gaining a better understanding of how mental models internally represent complex, dynamic systems and how these representations change over time will allow us to develop mechanisms to enhance effective management and use of natural resources. Realizing this potential, however, relies on developing and testing adequate tools and techniques to elicit these internal representations of the world effectively. This paper provides an interdisciplinary synthesis of the literature that has contributed to the theoretical development and practical application of the mental model construct. It explores the utility and applicability of the construct in the context of NRM and includes a review of elicitation techniques used within the field. The major theoretical and practical challenges that arise in drawing on the construct to provide a cognitive dimension to NRM are also addressed.

Our intent was to provide a methodological overview of the primary data collection process in support of the other articles in this special issue. We documented and illustrated the use of a data collection and analysis suite, SenseMaker, that was designed to collect and work with narrative fragments. The approach presented adds a new and inherently mixed tool to the mixed methods toolbox. Despite its novelty and potential utility, little has been written in the academic literature on the application of SenseMaker to complex problems. To the best of our knowledge, the approach has not been used in relation to climate change or climate change adaptation and has not been presented in the mixed methods literature. We sought to contribute to filling this gap through describing the approach used to generate the data that underpin the articles in this special feature. Our purpose was to illustrate some of the potential and most notable challenges of using the SenseMaker data collection and analysis process in a complex domain through examining adaptation to climate change. Our overview was not exhaustive but rather sought to highlight capabilities and challenges through examining experiences of adaptation from a stages of change perspective. SenseMaker provides a remarkably powerful tool for the capture of micronarratives of complex phenomena such as climate change. The capacity to have respondents interpret, i.e., make sense of, their own narratives is an important innovation that provides one plausible solution to the problem of analysts coding narratives. Analytically, however, SenseMaker is relatively weak for those seeking strong statistical support for analyses and provides no capability for analyzing the narratives themselves.

As the Global Programme to Eliminate Lymphatic Filariasis (LF) approaches its 2020 goal, an increasing number of districts will enter the endgame phase where drug coverage rates from mass drug administration (MDA) are used to assess whether MDA can be stopped. As reported, the gap between reported and actual drug coverage in some contexts has overestimated the true rates, thus causing premature administration of transmission assessment surveys (TAS) that detect ongoing LF transmission. In these cases, districts must continue with additional rounds of MDA. Two districts in Indonesia (Agam District, Depok City) fit this criteria-one had not met the pre-TAS criteria and the other, had not passed the TAS criteria. In both cases, the district health teams needed insight into their drug delivery programs in order to improve drug coverage in the subsequent MDA rounds. To inform the subsequent MDA round, a micronarrative survey tool was developed to capture community members' experience with MDA and the social realm where drug delivery and compliance occur. A baseline survey was implemented after the 2013 MDA in endemic communities in both districts using the EPI sampling criteria (n = 806). Compliance in the last MDA was associated with perceived importance of the LF drugs for health (p<0.001); perceived safety of the LF drugs (p<0.001) and knowing someone in the household has complied (p<0.001). Results indicated that specialized messages were needed to reach women and younger men. Both districts used these recommendations to implement changes to their MDA without additional financial support. An endline survey was performed after the 2014 MDA using the same sampling criteria (n = 811). Reported compliance in the last MDA improved in both districts from 57% to 77% (p<0.05). Those who reported having ever taken the LF drug rose from 79% to 90% (p<0.001) in both sites. Micronarrative surveys were shown to be a valid and effective tool to detect operational issues within MDA programs. District health staff felt ownership of the results, implementing feasible changes to their programs that resulted in significant improvements to coverage and compliance in the subsequent MDA. This kind of implementation research using a micronarrative survey tool could benefit underperforming MDA programs as well as other disease control programs where a deeper understanding is needed to improve healthcare delivery.

We survey and evaluate selected participatory tools that have been proven effective in natural resources management and research during our extensive experience with forest communities. We first establish a framework for our analysis by identifying a set of criteria for evaluating each tool. Next we provide a brief description of each tool, followed by an evaluation and comparison of the strengths and weaknesses of all the tools examined and how well they can be adapted to diverse contexts. We also provide suggestions for avoiding common pitfalls. Our findings suggest that most tools are flexible enough to be adapted to a range of applications, and that results are more robust when tools are used in concert. Practitioners should not be disturbed when results are contradictory or unexpected; initial surprises can lead to unexpected discoveries. Given the complexity of natural resources and their management, picking the right tool does not guarantee that the data desired will be produced, but selecting the wrong tool does make success less likely. The tools assessed are Bayesian belief networks and system dynamic modeling tools, discourse-based valuation, the 4Rs framework, participatory mapping, scoring or the Pebble Distribution Method, future scenarios, spidergrams, Venn diagrams, and Who Counts Matrices.

The sustainable management of natural resources largely depends on people’s conceptions of environmental systems and how they function. The mental model construct provides an appropriate means to explore the cognitive dimension of people’s interactions with such systems. Mental models are cognitive representations of external reality that people use as the basis for acting with and within the world around them. We aimed to improve the application of the mental model construct to the field of natural resource management, with an emphasis on creek, i.e., stream, systems, by exploring how certain elicitation procedures may affect the mental models expressed. One of the initial hurdles that must be overcome is to work out how to effectively elicit people’s mental models of complex, dynamic phenomena. By improving their understanding of mental model elicitation procedures, researchers can make better use of the mental model construct to further explore the cognitive and social dimensions of human–environment interactions. The procedures compared were oral interviews and a drawing task with oral commentary, conducted at either a creek location, where visual cues were available, or in the interviewee’s home. We found that the location of the interview had a greater effect on the expressed mental models than the interview task. The locations also evoked different emphases in the mental models: those elicited by a creek featured more concepts and were more specific, whereas those elicited at home were typically more generic and dense. The interview task was found to have minimal effect on the mental models expressed.

The mass media has ensured that the challenging and complex phenomenon of climate change now has the household familiarity of a brand name. But what is it that is understood by climate change, and by whom? What frame of reference is drawn upon to communicate meaningfully about climate change? Do particular subgroups within our society hold different understandings, or have the debate and the prolific dissemination of information about this issue coalesced around a core perception or image of what climate change is? To answer these questions, we conceptualized climate change within the theory of social representations as emergent socially constructed knowledge. We analyzed word association data collected in Australia from persons identifying as having a scientific, government, or general public background (N= 3300). All respondents were asked to write the first words that came to mind when they thought about climate change. Comparative analyses of the word associations reveal that respondents from different backgrounds define climate change in different ways. The results suggest that there is a common core set of concepts shared by the different groups, but there are also a great many differences in how climate change is framed and conceived by respondents. The results are discussed in relation to what they imply for responses to climate change by these social groups and in relation to interventions designed to encourage climate adaptation.

The content, structure, and distribution of mental models can be elicited and measured using a variety of methods. In this article we explore a method for eliciting mental models within the context of water use and management in South Africa. This method is consensus analysis, a technique developed in cognitive anthropology. We used it to analyze qualitative data from semistructured interviews, pilesorts, and questionnaires to test quantitatively the degree of sharing and diversity of mental models within and across social groups. The consensus analysis method focused on comparing the mental models of two key stakeholder groups in the Crocodile River catchment in South Africa, i.e., conservationists and irrigators, to better understand the level of consensus between these groups. We specifically investigated the level of agreement regarding: (1) major water users of the Crocodile River, (2) causes of the current problems with flows in the river, (3) consequences of the river not flowing, and 4) priorities for future use. We discuss the results and examine the strengths and challenges of consensus analysis for eliciting and measuring mental models. We also evaluated the usefulness of this method in assisting natural resource managers to identify strategies for improving integrated management of water resources.