Understanding social license for nature-based coastal adaptation: a longitudinal culturomic approach
Dr. Kate Sherren, Dalhousie University, School for Resource and Environmental Studies
Enda Murphy Senior Research Engineer, Oceans, Coastal and River Engineering National Research Council
Dr. Patricia Manuel, Dalhousie University, School of Planning
Dr. Mike Smit, Dalhousie University, School of Information Management
This project will bring together new developments from social sciences and humanities work in the interdisciplinary fields of social impact assessment (SIA), marine spatial planning (MSP) and landscape culturomics to apply to nature-based infrastructure implementation projects. The objectives will be both to develop and pilot replicable methods for understanding the social dimensions of nature-based systems implementation, and assist NRC in deepening its capacity for integrating social sciences and humanities scholarship in its own research projects.
Community members see and experience their landscapes in complex ways that shape how they perceive new options for coastal flood risk management. The political will to implement nature-based options will falter if the social dimensions of such options are not given equivalent attention to the technical dimensions.
- IDPhD program minimum entry requirements are an A- (3.7) GPA at the undergraduate and graduate degree level.
- The position will be suitable for a student with previous degrees in social science disciplines or interdisciplinary studies that include social science, and will have had some exposure to interdisciplinary or multi-disciplinary research programs. Disciplines include, but are not limited to, social geography, planning, information science, sociology and cultural anthropology, environmental studies, natural resources management, marine studies, among others.
- Students will be skilled in social science research methods, and ideally have experience in social impact assessment or social license research.
- Experience with IT including programming and systems work is an asset, but is not required, as the increased sophistication and usability of machine learning tools means leveraging this technology is a teachable skill.