Design thinking for supporting citizens in climate change adaptation
Bibliographic record
Abstract
Design thinking, which includes consideration of user needs, abduction and rapid prototyping, is a promising collaborative approach in education for sustainability. Our research team has experimented design thinking with citizens on various environmental issues, including climate change adaptation. Our results demonstrate that design thinking promotes a broader understanding of the issues by the participants, motivates them, strengthens their empathy towards users, identifies their real needs, leads to many solutions, while mobilizing certain high-level skills. However, design thinking requires time and multidisciplinary work. It is limited by the knowledge of the solvers, by the short-term consideration of the problems and by the emphasis placed on the human being. What design thinking process would be optimal to support citizens in adapting to climate change? Climate change is a complex environmental problem. In this area, resolvers who wish to adapt must deal with poorly defined initial situations and unpredictable climatic risks. For example, if little is known about the state of health of a watercourse and the lifestyles of local residents, it will be difficult to propose adaptations that will make the watercourse water and citizens resilient to floods or droughts. An understanding of the initial social, ecological and economic situations is therefore necessary to properly target the adaptations that will be applied locally. During a process of adaptation to climate change, problem solvers must therefore be invited to properly represent the social and scientific issues of climate change and to mobilize their systemic and forward thinking. Knowing each their share of local situations, resolvers must share their perspectives to collectively compose a credible portrait of situations, sub-situations and likely subsequent situations. The formulation of solutions must also mobilize certain skills in the solvers, including creativity, critical thinking and strategic planning. These desired qualities for the solutions require a support process that invites the solvers to mobilize their capacities for innovation, critical judgment and planning. In the ClimAction program, we designed a design thinking process, conducive to education on adaptation to climate change. High school students are challenged to improve a local waterway to make it more resilient to floods or droughts. The program uses design thinking as well as the mobilization of adaptation skills: systemic, predictive, creative and critical thinking; communication; strategic planning. Students study the health of a local stream using scientific indicators: presence of macro-invertebrates and health of fish. They interview local citizens to find out their needs and uses of the watercourse. They then represent the initial situation of the river in its social and scientific aspects. They predict the possible impacts of floods and droughts on the watercourse. Ideation and rapid prototyping allow them to propose, choose and implement an adaptation action that they communicate to the population. The ClimAction program uses techniques of visual representation, helping students to structure an environmental issue and to predict the possible futures of a watercourse. We also want to develop their feelings of being able to act. The presentation sums up our research on design thinking and discusses the ClimAction program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".