What Can Visualization Research Do for Climate? A Workshop Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 it