130
J. Zscheischler
involving risk and environment, in what we call ‘post normal science’, we can think
of it as one where facts are uncertain, values in dispute, stakes high and decisions
urgent.” (Funtowicz and Ravetz, 1993).
The authors argue that these challenges and problems are virtually impossible to explain by the dominating reductionist research approaches in science.
Instead, systemic and synthesising approaches are required to tackle problems with
a high degree of “unpredictability, incomplete control and plurality of legitimate
perspectives.”
In this context, the authors postulate that science has a strong responsibility for
societal development. They refer to the history of progress that has been successfully pushed by scientific knowledge. However, they also voice criticism: “…After
centuries of triumph and optimism, science is now called on to remedy the pathologies
of the global industrial system of which it forms the basis.” (ibid.)
Many aspects of “post-normal science”, such as “grasping complexity”, “dealing
with uncertainty” or “accounting a diversity of perceptions” (e.g. Mobjork 2010;
Pohl and Hirsch Hadorn 2008), have been adopted and incorporated in the discourse
of TD.
To date, practical applications of TDR can be found in the field of integrated
environmental or sustainability science, as well as in health science (e.g. Klein 2008;
Bammer 2005). In fact, sustainability science appears to be the ideal designated
field for TDR (Hirsch Hadorn et al. 2006; Scholz and Steiner 2015). In this field,
TDR is based on the derivation of a changed perception of great challenges and
political objectives such as the Sustainable Development Goals (SDGs); it is backed
by politically motivated funding programmes
5 for sustainability research.
Today, science is not only expected to understand and explain phenomena, but also
to provide guidance for action. Hence, knowledge production is called on to handle
normative orientation and interrelate “descriptive, normative and practice-oriented
forms of knowledge” (Pohl and Hirsch Hadorn 2008). This differentiation into the
above three types of knowledge was discussed by several authors, who divided topics
into (i) systems knowledge, (ii) target knowledge, and (iii) transformation knowledge
(Jantsch 1972; Wiek 2007; Zierhofer and Burger 2007; Schäfer et al. 2010). Systems
knowledge refers to questions about characteristics and dynamics of a problem,
considering complex human–environment interactions and diverse interpretations
(Know what?). Target knowledge represents normative knowledge, and captures
desired goals and the needs and direction for change (Know where?). Transformation knowledge incorporates support for the development of strategies for societal
transformation processes and concrete action (Know how?).
A similar differentiation into knowledge types can also be found in the concept put
forward by Max-Neef (2005), who outlined his idea of a “transdiscipline” by interrelating the specialised disciplines taught by modern-day universities (see Fig. 7.1).
Max-Neef distinguished between four different levels: At the basic “empirical” level
5 In Germany, this is especially supported and funded by the Federal Ministry of Education and
Research.
J. Zscheischler
involving risk and environment, in what we call ‘post normal science’, we can think
of it as one where facts are uncertain, values in dispute, stakes high and decisions
urgent.” (Funtowicz and Ravetz, 1993).
The authors argue that these challenges and problems are virtually impossible to explain by the dominating reductionist research approaches in science.
Instead, systemic and synthesising approaches are required to tackle problems with
a high degree of “unpredictability, incomplete control and plurality of legitimate
perspectives.”
In this context, the authors postulate that science has a strong responsibility for
societal development. They refer to the history of progress that has been successfully pushed by scientific knowledge. However, they also voice criticism: “…After
centuries of triumph and optimism, science is now called on to remedy the pathologies
of the global industrial system of which it forms the basis.” (ibid.)
Many aspects of “post-normal science”, such as “grasping complexity”, “dealing
with uncertainty” or “accounting a diversity of perceptions” (e.g. Mobjork 2010;
Pohl and Hirsch Hadorn 2008), have been adopted and incorporated in the discourse
of TD.
To date, practical applications of TDR can be found in the field of integrated
environmental or sustainability science, as well as in health science (e.g. Klein 2008;
Bammer 2005). In fact, sustainability science appears to be the ideal designated
field for TDR (Hirsch Hadorn et al. 2006; Scholz and Steiner 2015). In this field,
TDR is based on the derivation of a changed perception of great challenges and
political objectives such as the Sustainable Development Goals (SDGs); it is backed
by politically motivated funding programmes
5 for sustainability research.
Today, science is not only expected to understand and explain phenomena, but also
to provide guidance for action. Hence, knowledge production is called on to handle
normative orientation and interrelate “descriptive, normative and practice-oriented
forms of knowledge” (Pohl and Hirsch Hadorn 2008). This differentiation into the
above three types of knowledge was discussed by several authors, who divided topics
into (i) systems knowledge, (ii) target knowledge, and (iii) transformation knowledge
(Jantsch 1972; Wiek 2007; Zierhofer and Burger 2007; Schäfer et al. 2010). Systems
knowledge refers to questions about characteristics and dynamics of a problem,
considering complex human–environment interactions and diverse interpretations
(Know what?). Target knowledge represents normative knowledge, and captures
desired goals and the needs and direction for change (Know where?). Transformation knowledge incorporates support for the development of strategies for societal
transformation processes and concrete action (Know how?).
A similar differentiation into knowledge types can also be found in the concept put
forward by Max-Neef (2005), who outlined his idea of a “transdiscipline” by interrelating the specialised disciplines taught by modern-day universities (see Fig. 7.1).
Max-Neef distinguished between four different levels: At the basic “empirical” level
5 In Germany, this is especially supported and funded by the Federal Ministry of Education and
Research.