Visualizations as a tool to increase community engagement in climate change adaptation decision-making
Bibliographic record
Abstract
Many barriers to behavioural change exist when it comes to climate change action. A key element to overcoming some of these barriers is effective communication of complex scientific information. The use of visualizations, such as photographs or interactive maps, can increase knowledge dissemination, helping community members understand climatic and environmental changes. These techniques have been utilized in many disciplines but have not been widely embraced by climate change scholars. This paper discusses the utility of climate change data visualization as a tool for climate change knowledge mobilization. This paper draws on the case studying drivers of coastline change of Lake Ontario in the Town of Lincoln, Ontario, Canada. Historical aerial photographs were used to measure the rate of coastline change and visualize vulnerable sections of the coast. To better visualize the changes that occurred over time from a resident viewpoint, selected land-based historical photographs were replicated by taking new photographs at the same locations. These visualization tools can be useful to support the community in developing strategies to adapt to climate change by increasing understanding of the changes and knowledge through social learning. These tools can be generalized to other case studies dealing with community engagement in coastal adaptation efforts.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".