Navigating the climate change minefield: the influence of metaphor on climate doomism
Bibliographic record
Abstract
Climate doomism is an increasing concern for climate change communication. In the United States, this opinion regarding anthropogenic climate change is now more prevalent than climate skepticism, and is the primary reason cited for opposition to climate action. Doomism is the belief that catastrophic warming of the planet is now inevitable, and that effective mitigation is impossible. The behaviors resulting from this view are comparable to the result of climate skepticism: doomism produces paralyzing eco-anxiety and subsequently inaction. Prior work has hypothesized that the rise in climate doomism and eco-anxiety is linked to climate change risk communication. This study investigates the possibility that the metaphoric language used to communicate the severity and urgency of climate change could inadvertently promote doomism. We employ a survey model to test the influence of metaphoric language on perception of urgency, feasibility, and individual agency in relation to the climate crisis. American English-speaking participants (N = 1,542) read a paragraph describing climate change either as a “cliff edge” or “minefield,” with human agency manipulated to be present or absent. Responses were considered to be doomist if they reported a high sense of urgency, paired with a low sense of feasibility and/or agency; this indicates they have a high awareness of the risks associated with the climate crisis, but a low belief that it will be addressed, and/or that their actions can produce meaningful change. Use of either metaphor improved perceived feasibility without a reduction in urgency, indicating that metaphor is an effective climate communication strategy for conveying risk without promoting doomism. However, metaphoric presentation is only effective when paired with human agency, suggesting that agency is a necessary component for successful metaphoric climate communication strategies.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".