2.1.1. Interdisciplinary and Transdisciplinary Research Methodology for Sustainability

Original text by [Stella Veciana].

Over the past few decades, a section of leading-edge science has been trying to go beyond traditional scientific methodology, by means of transdisciplinarity. The term transdisciplinarity was first used in the seventies in the context of a reassessment of the university education and research model. There is currently no single definition of transdisciplinarity, a concept that is constantly under discussion and subject to change. The sociologist Thomas Jahn sums it up as follows: “transdisciplinarity is a reflexive research approach that addresses societal problems by means of interdisciplinary collaboration as well as the collaboration between researchers and extra-scientific actors; its aim is to enable mutual learning processes between science and society; integration is the main cognitive challenge of the research process” (Jahn et al. 2012, 4). Transdisciplinary research takes into account three kinds of knowledge that are key to science geared towards sustainability: the analysis of the knowledge of the system, the attempt to reach an agreement on the desired target knowledge, and the required transformation knowledge (Becker/Jahn 2000). The transdisciplinary approach can be described as

- the systems knowledge that contains knowledge about the needs and challenges facing societies, and about the structures and contexts that they emerge from. This means considering questions that have to do with sustainability, such as: what is the relationship between local challenges and global challenges such as planetary limits, the non-sustainable use of the environment, poverty and social injustice? How can we guarantee that future generations will be able to survive and meet their needs? To answer these questions, researchers begin by studying the complex links between the social, ecological, economic, and cultural dimensions of the problem. The analysis of the context clarifies the current state of the problem and its specific conditions. The analysis also looks at the aspects of uncertainty in the problem in question. As such, it helps researchers to understand what knowledge is missing, to define socio-technological innovation, and to assess the possible consequences of innovation and of social actions.

- the target knowledge that makes it possible to integrate many different types of knowledge into a joint vision. For the research team, this can mean questioning pre-conceived ideas in order to arrive at common approaches in a non-hierarchical, intercultural dialogue. This involves pooling the targets of the collaboration between researchers and social actors. Based on the analysis of the current state of particular social problems, researchers study “the situation that should or should not be”. They also consider the desired impact of the project, which can result in laying the groundwork for future regulations, or to proposing sustainable lifestyles. Other challenges are linked to the process of transdisciplinary research itself.

- transformation knowledge refers to knowledge about methods and concepts that can be implemented as possible solutions to the problem in question. Based on this knowledge, researchers specify the personal training, resources, and social interventions required to reach the common objective. Transformation knowledge is knowledge about how to move from the current situation to the desired situation. This means that members of the research team will be required to have certain attributes during the entire transdisciplinary research process. One of these is to be open to dialogue, so as to build relationships based on mutual trust. Others are critical self-reflection, an awareness of co-responsibility, and commitment.

In interdisciplinary and transdisciplinary research, participatory methodology is a key element for creating more sustainable and resilient societies. On one hand, it promotes critical self-reflection and an awareness of co-responsibility in knowledge-generation and implementation processes. And on the other, it generates non-hierarchical dialogue based on mutual trust, which allows researchers to imagine alternatives to specific problems through common approaches. Notable precursors to the participatory method include Participatory Research-Action, Agricultural Systems Research, Rapid Rural Diagnosis, Participatory Rural Diagnostics, Participatory Monitoring and Evaluation, and Participatory Analysis of Poverty. From the beginning, participatory methodologies have been widely used around the world by different actors in a wide range of sectors. These methods were initially conceived and used by small non-government organisations and agricultural research centres for the design of rural development projects. Nowadays, they are used by universities, cooperation agencies, and even the World Bank. Moreover, their use has spread to projects in very different fields, including natural resource management, conflict resolution, microcredits, and healthcare, to name a few.

Transdisciplinary methodology begins with a study that integrates different types of knowledge. It is based on the idea of a structural change in the relationship between science and society. It uses relational methodology that combines scientific knowledge and practical knowledge. Notable concepts include "Mode 2 Investigation” (Gibbons et al, 1994), “postnormal science” (Funtowic/Ravetz, 2001) and the precursor of “research-action” (Kurt Lewin, 1946).

Perhaps one of the most significant precursors of transdisciplinarity is "action-research" methodology. According to its creator, MD and psychologist Kurt Lewin, the three most important characteristics of action-research are: its participatory nature, its democratic impulse, and its simultaneous contribution to knowledge in the social sciences. During and after World War II, Lewin and his colleagues at the Group Dynamics Centre at MIT embarked on several projects in conjunction with civil servants and community leaders. Their aim was to transform theoretical principles from the field of psychology into practical recommendations for solving social problems such as interracial conflicts (Lewin, 1948). The key ideas behind the participatory aspect were “group decision-making and commitment to improvement”.

Another important forerunner is the “postnormal science” methodology developed by the mathematicians Silvio Funtowic and Jerome Ravetz. This approach aims to resolve social or environmental situations, for example, in which "facts are uncertain, values in dispute, stakes high, and decisions urgent," (Funtowic/Ravetz, 1991). The uncertainty of the system and the consequent decision-making risks are particularly high in the case of major challenges such as climate change. The complexity of these problems requires preemptive, participatory legislation in order to prevent irreversible change. This means an “extended community of peers” promoting civic participation in order to provide multiple, constructive perspectives on such complex and controversial scientific and political issues.

From the nineties onwards, theorists such as the sociologists Helga Nowotny and Michael Gibbons began to make a distinction between the development of “mode 2 applied research” and “traditional mode 1 science”. Research problems in mode 1 arise exclusively from internal disciplinary scientific interests, while research lines in mode 2 seek “robust solutions” to social problems in a particular context. In order to increase the robustness of solutions, a deeper knowledge of the context is required. For example, the appropriate design and materials for a robust building will depend on whether the site on which it will be built is earthquake prone. Meanwhile, scientific quality in mode 2 is bound to social responsibility. The added value is the “integrated social value”, or in other words, the fact that it considers the social consequences of scientific production. Nowotny also proposes open communication between science and society, and advocates setting up an “agora” in which to discuss the knowledge required for the robust solutions that we need.