Storytelling for Making Cartographic Design Decisions for Climate Change Communication in the United States
Bibliographic record
Abstract
Recent research in cartography has described how maps can tell stories; however, little research has empirically evaluated how storytelling can guide how map design decisions are made. I argue that storytelling allows cartographers to decide on basic map design elements by narrowing the focus of a map. First, cartographers decide on the driving story. The story is then used as a guide for every design decision, from what data to search for and use to the design of symbolism within the map. This research focuses on the case of climate change communication in the United States. Empirical evidence based on interviews with map-makers at major media organizations and government agencies creating maps of climate change illustrates how storytelling as a process provided these cartographers with a way to effectively convey the multidimensional and complex impacts of climate change across multiple scales. It is this storytelling process that enables cartographers to better connect with readers to communicate the impacts of complex environmental problems such as climate change. The article concludes with implications for using storytelling as an alternative way to think about cartographic communication and the map design process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".