What Can Visualization Research Do for Climate? A Workshop Report
Bibliographic record
Abstract
Earth, our home planet, is changing at an unprecedented rate due to human industrial activity. Data visualization can uniquely illuminate these complex transformations by revealing hidden patterns, thereby translating abstract data into compelling narratives and increased understanding. How can we harness visualization's full potential to inform and inspire our generation toward environmental awareness and stewardship? This article reports on insights and key challenges from the 2024 IEEE VIS workshop on climate action and sustainability whose submissions paint a rich picture of the current, yet still nascent, landscape of how the field of visualization can help empower people to take meaningful steps toward environmental stewardship. Drawing from the presented works and the collective workshop discussions, we propose future research directions and invite the visualization community, both researchers and practitioners, to join this vital effort in addressing one of our planet's greatest challenges.
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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 | 0.044 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".