Black Summer arson: Examining the impact of climate misinformation and corrections on reasoning
Bibliographic record
Abstract
Climate misinformation has been identified as a barrier to mitigative action. One prominent example occurred when the 2019/2020 “Black Summer” bushfires in Australia were blamed on arson. This claim is cognitively attractive because of its simplicity and was widely publicised at the time, but also thoroughly debunked. In two experiments, we examined the impact of a fictional misleading article implicating arson as the primary cause of the Black Summer fires on Australian (Exp. 1, N = 509) and Canadian (Exp. 2, N = 506) participants’ reasoning, associated donation behaviour, and climate change attitudes. The misinformation significantly influenced reasoning about the Black Summer and future fires in both experiments; it also reduced the donations of Australian participants to a local climate organisation and impacted Canadian participants’ reasoning about a novel, conceptually related (but fictional) flooding event. Corrections were largely effective at mitigating misinformation impact. A bolstered correction that portrayed climate change as an important causal factor through its impact on risks and emphasised the multicausality of natural disasters was more effective than a simple correction that merely refuted the misinformation. Climate change attitudes were largely unaffected by the misinformation and interventions. Our findings demonstrate that event-specific climate misinformation can influence reasoning beyond a specific event, and that corrections are broadly useful for combatting its effects. • Extreme-weather misinformation can impact reasoning and donation behaviour • Event-specific misinformation can influence reasoning about related events • Corrections reduce the impact of misinformation on climate-related reasoning • Explaining the multicausality of weather events increases correction effectiveness • Climate attitudes are largely unaffected by extreme-weather misinformation
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".