APPRECIATIVE CONTEXT-BASED PROBLEM
FRAMING
A detailed analysis based on Case Study 3
In the previous chapter, I outlined the iterative process of progressive case study data collection and analysis alongside literature review and theory adaptation, which led to the proposed methodology in five principles for transdisciplinary research practice. In this chapter (and the four following chapters), I test the five principles and their associated pattern sets through a detailed analysis of data from Case Study 3.
Setting the scene: testing the pattern set for the first principle
In this chapter, I focus on a detailed analysis of Case Study 3, based on the first principle.
Principle number 1: A collective, inclusive approach to appreciative, context-based problem framing is needed to embrace the richness of complexity.
I test the pattern set I have associated with this principle (as outlined in Chapter 6), which is an adaptation of Ashhurst’s six, wicked problem dimensions, as a means to explore context-based problem definition in Case Study 3.
In Chapter 3, Weaver’s systems of organised complexity are self-organising systems with emerging characteristics and relationships, the source of new possibilities and dynamics, and a source of change and novel solutions. In a system of organised complexity, the problems are likely to be wicked. These are problems that defy complete definition, have no final solution, where any solution creates new problems and issues, and all solutions are partial and the best that can be done at the time.
Nailing down the problem statement is likely to be fraught with difficulty, and the conventional wisdom in dealing with this is to narrow the focus and pick a small number of variables thus risking oversimplification. However, as I outline in Chapter 3, this can also narrow the possibilities for novel solutions and new thinking, and can increase the risk of failure. Also, the conception of the problem will vary vastly between different disciplinary and non-disciplinary participants (where none of these perspectives is necessarily wrong). Ramalingam (2014) describes wicked problems as being difficult to define, where many potential explanations exist, there are multiple views and multiple potential solutions. In addition, it is nearly impossible to “stand outside” the problem and claim objectivity, as the researcher and the research team are by definition part of the context and exerting an influence on it by simply being there and asking questions.
In this chapter, I firstly investigate the complexity of the research context for Case Study 3, and secondly, I bring together the different perspectives of the researchers to build a more systemic picture of the situation using Ashhurst’s (2014) modified six wicked problem dimensions as an heuristic or analytical tool for transdisciplinary research teams. The six experiential dimensions (as previously outlined in Chapter 3) are instability, ambiguity, intractability, confounding factors, complexity and diversity. The dimensions overlap and a particular problem attribute can be grouped in more than one of these dimensions. This framework provides a way to organise this information into patterns (but not discrete entities). This aligns with Alexander’s idea of the pattern language.
Chapter 8 Appreciative context-based problem framing
In reporting on the findings in this chapter, I have described each wicked problem dimension separately. Within each dimension, I have first described my interpretation of the concepts captured by each dimension (Concept), followed by a summary of the findings that I have coded according to each dimension (Findings) and lastly my interpretation (Interpretation).
Instability dimension
Concept
The instability dimension highlights the dynamic nature of wicked problems, and that these problems are unstable and constantly evolving from a number of perspectives. It encompasses the degree of change as well as different rates of change in different parts of the system. It covers a number of different kinds of change, including changes in thinking, physical and social situations, as well as the unpredictable nature of complex systems.
Findings
Household typologies and instability
A key component of the project was developing a set of household typologies to try to capture some of the diversity within farming systems and households. CS3A said “What has clearly come out of the work in both Laos and Cambodia and India, is that farming and diversity of income streams have changed dramatically in these communities. So that they are no longer full-time farmers, they are looking at external income generation. So I think that is very important, that the kind of smallholder farmer is changing very rapidly, they are becoming a lot more diverse in their income generating
schemes.” This is a good example of instability in a research situation, where studying
households according to the sustainable livelihoods framework requires careful thought in that the livelihood profile at the time of the study can have changed completely (and rapidly) through the life of the project (through both internal and external factors). The wider team was well aware of this − and reflected this in their discussions with me, and in their methodology and approach, which changed as their learning progressed. CS3C said, “I suddenly realised how narrow the adaptive capacity assessment stuff was, and the livelihood stuff was, because it’s static. It doesn’t give you trajectories and
… it’s not straightforward in how you scale it. … So our experience with the types has sort of led to this realisation that households are dynamic and the types don’t necessarily reflect that … but also urbanisation, migration, all these other development
trajectories that are likely to override or derail if you just focus on climate.”
CS3D pointed out the importance of history and what has come before, “that’s where you need to look at 30 years of history to be confident that you’re not leading people up
the wrong path.”
He also highlighted that the farmers he was working with were dealing with a very rapid rate of change. “In a way, there’s been a bit of a transition from traditional ways of farming to a kind of farming that the industrial revolution has brought along. There’s a very rapid change. For example, when this project started, there was almost no
mechanisation on these farms. Now I think maybe 80% of rice harvest is mechanised.”
The rapid shift to mechanisation was talked about a great deal in the project. CS3P: “… we’ve seen it already in the two or three years we’ve been there, mechanisation of, or the use of two-wheel tractors as labour gets harder to find, they’re looking for ways of improving things … or speeding up things like planting and harvesting. So I think you
are going to see increases in harvester numbers.”
Climate change and instability
One of the key issues highlighted by CS3B was the goal of planning for climate adaptation for 2030 or 2050. “With rapid change already happening, so much can change in that time and no one has any definitive idea about what the farming systems and landscape will look like, nor what other social and environmental change may
happen in such a complex system.”
More importantly, many participants noted that farmers were not concerned with what might happen in 30, 40 years or at the end of the century, but were focused on surviving and flourishing now. CS3B: “‘Well have you any idea of what the future farming landscape will be in 2030, 2050 for which you want to be effectively adapting?’ and the
answer was ‘no’ … just dealing with climate variability is the best way to try and build
Chapter 8 Appreciative context-based problem framing
CS3F said, “You know, there is a view of adaptation to climate change, as a sort of transformational change. I don’t think in any of the countries here we are looking at transformational change. … I think most of us are doing things to reduce risk of catastrophic change, catastrophic damage to crops or just give them a couple more
strings to the bow in terms of their livelihoods and adaptability.”
Project team staffing and instability – an Achilles heel
CS3B talked about the problem of staff movement and the rapidity with which people change jobs. “I mean people move, I think that’s your biggest challenge in any of these projects. The main effect is, you could say a loss of continuity in the project, in having to go back and start a learning process … which is time consuming and has implications. These are internal [organisational] issues. So, yeah, everyone is moving.”
The project leader reported on the challenge of staff “churn”, particularly around the leadership team, and the need for replacements as members of his leadership team (deputy project leader and the project coordinator) moved on to other jobs.
He said, “So when X left, I said, okay, I’ve got Y. When Y left, I said, ‘I’ve got a problem’. And I am frantically trying to get people into a position that they can continue the project without me. But right now, with the current complement of staff, I
don’t have people yet ready to do it on their own.”
He also reflected on the need for what he called the integration skill set and commented that there seemed to be a limited pool of people around who have these skills and abilities, and that at that time, there was really no one who could take on his role if he was no longer able to continue. “And that is the Achilles Heel of this project, that it is
still too dependent on [specific people].”
This instability of staffing applied across all of the country partners. Many of the participants talked about the multiple changes in staffing in the teams they worked with across countries. CS3F said, “So it happens fairly often in [country], they start out projects, sort of roll out the ‘A’ team. … And bit-by-bit they got hooked up in other projects. … Once the project was up and running, then they would say, ‘Ah, we have the ‘B’ team. Yeah, so you have to go through all the [training and team building] again … And we’ve actually got the ‘C’ team as well’.”
This project experienced frequent changes in personnel both at the funding agency level (up to four program managers even before the second half of the project), and within the project team itself.
Timing is everything
One of the less visible elements of instability for the research team was the shifts in policy which were happening rapidly in the background. At the start of the project, climate change adaptation and mitigation were top priority policy issues for Australia. By the time the project was reaching its conclusion, climate change had become virtually unmentionable within government agencies in Australia. This had significant implications for potential follow-on work and research implementation. It also demonstrates the stark difference between policy (or rather political) timeframes and research life cycles.
CS3D talked about the importance of timing with regard to focusing on issues like climate change. “It was seen that [country name omitted] wasn’t ready for climate change to be the focus of a project when I started [my project] not because of the project … but events around the world [meant] that [the] emphasis changed so it became open to that focus. It just happened … the world [focus] around climate change.”
Interpretation
The findings in the previous section create a picture of a dynamic, rapidly changing context, including rapid changes in the farming communities and farm households livelihoods being studied, against a background of rapid regional change. These factors included increasing mechanisation and the rapidly escalating cost and scarcity of labour. Sitting behind this are the various forces associated with globalisation; for example, the rapid urbanisation of populations in many parts of the developing (and developed) world, and the growing connectedness. Ramalingam talks about the increasing risks associated with greater connectedness, for example, vulnerability to economic downturns and shocks (2014). Not only did the researchers need to observe and understand this instability, they needed to see this as integral to the research context and problem.
Chapter 8 Appreciative context-based problem framing
This rapid change is reflected in the dynamic nature of the project team, characterised by the rapid turnover of staff, changes in institutional environment and wider policy changes and adjustments by the funding agencies. Even more importantly, it highlights the need to see change within the research team and associated institutions as part of the research context, rather than an external (or non-project) factor. The extension of this is that the researchers themselves, the team they are part of, and the institutions they are associated with, are an integral part of the research landscape, rather than sitting outside of the research situation and looking in.
While many of the researchers spoke to me about the issue of instability and unpredictability and their approaches to this in the interview sessions and amongst themselves, this was not explicit in the project proposal or design, though these issues were reported somewhat marginally in the progress reports. Given that capacity building and learning and development were objectives of the project, I suggest that it would have been helpful to be more inclusive of these contextual issues, such as rapid changes in personnel, for more comprehensive and informative problem framing.
The extension of this is the idea that no researcher can be truly objective or stand outside the context they are researching. While this is accepted by many of the more qualitative social science disciplines, striving for objectivity is still a key goal for most aspects of quantitative research (post-positivist).
The findings demonstrate a degree of researcher adaptability to these sources of instability. In addition, and importantly, a number of the researchers identified some of the instability elements as opportunities for solutions and improvements. An example of this is the focus on climate variability. Rather than trying to develop adaptation options for a distant and unpredictable future, the project was instead adjusted to focus on identifying adaptations and coping mechanisms for short-term variability (and unpredictability) in climate and a range of other non-climate social, economic and physical factors. A second example is the gradual evolution of the household typology approach towards a livelihoods trajectory. In conclusion, the instability dimension illustrates the changing nature of the context and the need to regularly re-evaluate the research problem and context.
Ambiguity dimension
Concept
Ambiguity is taken to mean vagueness, fuzziness of meaning, authority, technology, goals or action. Ambiguity also covers multiple, indistinct, incoherent or fragmented meanings. This can include different interpretations of data, and lack of or uncertain data. It includes contradictory underlying cultural and/or tacit meanings, and distributed or unclear responsibility or lines of causality that can lead to a relatively large number of possible actions, approaches or solutions. It can also include an absence of authority. Taken together, this leads to uncertainty and unpredictability. There were a number of instances of this in the data.
Findings
The subject of the project – climate adaptation – was ambiguous in itself. The mid-term review report summed this up very clearly: “Notions of adaptation, climate change and
farming systems are elastic terms that can be stretched to include almost anything.”
And, “There is widespread consensus per the fact that the process of adaptation is ill-
understood.”
In particular, the researchers talked about trying to gain a shared understanding of climate adaptation with the farmers. CS3D: “It was actually very difficult at first to I guess elicit from farmers what, how they were already adapting and that’s sort of taken us a long time, maybe we’re still learning about that. So part of that is I guess just the difficulty of communicating in our cross languages, but also I suppose their
understanding of what we mean by that evolves …”
CS3D said, “The [farmers] had a very explicit understanding that climate was variable, and in what way it could vary from year to year. But when I asked the [farmers] question, ‘So what do you do different in different seasons?’ … the answer was unequivocally ‘we do the same thing every year’ … It’s totally untrue, and it’s taking a long time I guess to work out what they do differently. … There isn’t a shared language
or a shared knowledge about it.”
Chapter 8 Appreciative context-based problem framing
delve into what are the real constraints these villagers and farmers have in terms of what they can do to adapt. Otherwise, if you just have a bunch of farming systems modellers … I can easily see you coming up with things that look great on the computer
model or journal page, but are [not meeting the needs of the farmers].”
These two perspectives highlight the difficulties of creating shared meaning between different languages and cultures, and provide some examples of the potential misunderstandings and ambiguities that can result.
Another example of ambiguity was the discussion about feminisation of agriculture, and the lack of clarity about what this meant. CS3L: “… you feel there is a feminisation of agriculture, but not in the traditional sense where more women are getting into the formal agricultural activities. … The new generations of women, the daughters and the daughters-in-law, they are less likely to participate in agricultural work. But I was uncomfortable. I kept telling [the project team] to go slow [in characterising this process], because we have the census coming up which will give us a real picture of
women heading households.”
In addition to the issue of feminisation, there was some ambiguity or fuzziness in relation to the household typologies. Not only did different team members give a different account of what they meant and how they were being used in the research, the reviewers themselves seemed unclear. The review report states, “The project has developed a typology of households and attempts to link it to appropriate adaptation options. The typology has retained three key characteristics of households: (1) access to land, (2) access to water, (3) options for labour and remittances, (4) castes and status (in South Asia). The household research may prove useful in the formulation of policy recommendations but at this stage, the typology is limited in scope and does not do
justice to the amount of data collected by the project team.”
Rather than seeing this as a criticism of the approach or results, I take this to be evidence of the confusion around the typologies and what they mean and a direct result of the complexity, multiplicity and ambiguity of rural households in general – yet another challenge for the project team.