From science to story: communicating permafrost concepts with data comics
Bibliographic record
Abstract
We are creating data comics that use graphics, narratives and visualization to explain permafrost and its interaction with climate change. Despite the increasing attention to permafrost change due to its local impacts and interactions with global climate, many people without scientific background or lived experience related to permafrost do not understand what permafrost is or why it is important. This knowledge gap reduces public consideration and risk perception. We are exploring new ways to present this information to a wider audience, including policymakers, scientists from other fields, school teachers, and the general public. A major communication challenge we face is that many scientific articles are not easily comprehensible and understanding concepts such as permafrost thaw and its effect on land use and infrastructure can be challenging. To address this challenge, we are developing new ideas in creating data comics, a new format that integrates data visualization and storytelling to deliver insights from data in a new format. We are exploring the use of relatable examples and analogies to make scientific information more comprehensible to the public. We are creating data comics collaboratively with experts in data visualization, narrative construction, data comics, and permafrost science. The data comics are designed to be both scientifically informed and verified, using the best and most current scientific information available. We prioritize data transparency, working towards more understandable and engaging presentations of scientific concepts
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 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".