Dead in the water: Mortality messaging in water crisis communication and implications for pro‐environmental outcomes
Bibliographic record
Abstract
Abstract All nature relies on water, yet climate change threatens water availability to the highest degree—from too much (e.g. extreme weather; flooding) to too little (e.g. droughts; wildfires). These water shifts threaten all life on earth. Societies' safe and reliable water accessibility faces growing uncertainty from climate change; however, water crisis communication may inadvertently remind audiences of their mortality. According to terror management theory, these mortality reminders can hinder pro‐environmental efforts in humans and even increase intergroup biases—a significant challenge for developing environmental solutions. While climate change has been examined as a mortality reminder, water remains untested. We presented participants with either a mortality‐laden message, an aversive but not‐life‐threatening message, or one of three threatening water‐related messages—experiencing drowning, dehydration or contaminated water consumption—to determine if the water‐related messages function similarly to the mortality message. Some (e.g. drowning; contaminated water), but not all (e.g. dehydration), water messages increased death‐thought accessibility, which could lead to paradoxical environmental behaviours, depending on the audience. Our research findings should inform policymakers, non‐profit organizations and other water correspondents' communication strategies. As some threatening water messages elicit similar responses to known mortality reminders, the way water crises are framed is important for water‐related decision‐making and ensuring equitable, successful pro‐environmental outcomes. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 | 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".