Visual Climate Change Communication: From Iconography to Locally Framed 3D Visualization
Bibliographic record
Abstract
Climate change is an urgent problem with implications registered not only globally, but also on national and local scales. It is a particularly challenging case of environmental communication because its main cause, greenhouse gas emissions, is invisible. The predominant approach of making climate change visible is the use of iconic, often affective, imagery. Literature on the iconography of climate change shows that global iconic motifs, such as polar bears, have contributed to a public perception of the problem as spatially and temporally remote. This paper proposes an alternative approach to global climate change icons by focusing on recognizable representations of local impacts within an interactive game environment. This approach was implemented and tested in a research project based on the municipality of Delta, British Columbia. A major outcome of the research is Future Delta, an interactive educational game featuring 3D visualizations and simulation tools for climate change adaptation and mitigation future scenarios. The empirical evaluation is based on quantitative pre/post-game play questionnaires with 18 students and 10 qualitative expert interviews. The findings support the assumption that interactive 3D imagery is effective in communicating climate change. The quantitative post-questionnaires particularly highlight a shift in support of more local responsibility.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".