Seeing Climate Change: A Framework for Understanding Visualizations for Climate Adaptation
Bibliographic record
Abstract
Climate change has resulted in the need for adaptation tools to provide stakeholders with the ability to respond to a broad range of potential impacts. Geovisualizations serve as powerful engagement tools due to their capacity in communicating complex climate data to various audiences. Studies have also shown a preference towards conveying climate data through geo-visual representations, to quickly present ideas rooted in geographical challenges and solutions. However, a rapid pace of technological advancements has paved the way for an abundance of geovisualization products that have eclipsed the necessary theoretical inquiry and knowledge required to establish effective visualization principles. This study addresses this research gap through a two-step process of (1) conducting a thorough review of the geovisualization for climate change literature, and (2) creating a conceptual framework that classifies existing geovisualization products into themes relating to visualization features, audiences, and the intended outcome or purpose of the visualization medium. The result is the Climate Visualizations for Adaptation Products (CVAP) framework, a tool for researchers and practitioners to use as a decision support system to discern an appropriate type of geovisualization product to implement within a specific use case or audience. Visualizations with more interactivity were favoured among expert user groups, to act as tools for knowledge discovery. This is contrary to the visualizations intended for communicating a known message to a user group, as those products often had a low level of interactivity associated with their use.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".