Visual Representations of Climate Change - A Case Study of Canada
Bibliographic record
Abstract
Understanding how environmental problems, including Climate Change (CC), are visualized by the public and the media is crucial to developing effective communications strategies aimed at encouraging mitigation and adaptation behaviors. In this study, we sought to understand how Canadians visualize CC, the affective response elicited by CC images, and what factors predict the representativeness of photographs depicting CC. A representative sample of Canadian adult Anglophones (n = 618) completed an online survey that assessed responses to CC imagery and corresponding affective content (PANAS). Measures of demographics, CC beliefs/knowledge, and environmental values (NEP) were also collected. Content analysis showed Canadians mainly associate CC with ice melt, temperature, pollution, and flooding imagery. Logistic regression showed that CC representativeness of several photos is predicted by pro-environmental values, belief in the causes of CC, and political affiliation. Images generally elicited negative affect, particularly those depicting anthropogenic causes of CC, where feelings of distress and upset were strong. Importantly, CC images identified by participants differ from those commonly used in the Canadian news media. These findings will aid communicators in optimizing the use of visuals in CC messaging, and offer some guidance for more effective communication within the challenging Canadian context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".