Sensitivity in transdisciplinary projects: A case of reindeer management in Finland

• Problem oriented TD knowledge co-production faces various challenges.
• Roles of ‘reflective scientist’, ‘facilitator’, ‘intermediary’ and ‘capacity builder’.
• To perform roles successfully various sensitivities are needed.
• Acknowledging ‘owners’ of policy process and creating ownership to TD knowledge helps.
• Early and iterative multi-actor participation useful in TD knowledge production.

Knowledge integration in transdisciplinary projects can help to achieve a more comprehensive, balanced and relevant understanding of real-world problems and their potential solutions. In this paper, a project on reindeer management in northern Finland, RENMAN, is used to identify and examine some challenges that transdisciplinary projects face regarding knowledge integration and the roles taken by scientists to overcome the challenges as well as to explain why integration has or has not been successful. We identify and examine four challenges to knowledge integration: (1) the need for integration during the design of study questions and settings, (2) the need to manage competitive deliberative settings, (3) the potential mismatch between providing “optimal” policy recommendations and outputs integrating facts and values, and (4) the need to harness the benefits of the process to contribute to managing the problem in the future. Scientist in the RENMAN project used four roles to cope with these challenges: reflective scientist, intermediary, facilitator or capacity builder. Our key argument is that the metaphor of sensitivity can often explain the successes and failures regarding integration pursued by using the above mentioned roles. Sensitivity in transdisciplinary research encompasses attention toward the needs and problem definitions of knowledge holders and knowledge users, respect toward various worldviews, divergent perspectives and forms of knowledge, and the understanding of biases, power relations and possible marginalizations embedded in and resulting from knowledge production. Finally, flexibility to act on the knowledge gained through sensitivity is needed to design evolving processes able to contribute to the resolution of complex real-world problems.

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