Extreme Weather Events as Teachable Moments: Catalyzing Climate Change Learning and Action Through Conversation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extreme weather events are often described as teachable moments for climate change. In this article, we explore insights about the concept of teachable moments from healthcare literature and apply them to the climate change communication context. Specifically, we adapt Flocke et al.’s (2012. A teachable moment communication process for smoking cessation talk: description of a group randomized clinician-focused intervention. BMC Health Services Research, 12(1), 109. https://doi.org/10.1186/1472-6963-12-109) Teachable Moment Communication Process to offer a new dialogue-based communication framework that leverages extreme weather events as opportunities for environmental learning and action among the public. Our framework helps facilitate discussions about extreme weather events, with the goal of channeling dialogue into actions to address extreme weather-related risks at both individual and policy levels. An important nuance is delineating how climate change can exacerbate hazards, while vulnerability and exposure ultimately determine the impacts of hazards. We account for this distinction by centering our framework around the broader goal of reducing weather-related risks in diverse contexts including, but not limited to, climate change considerations. This article describes our proposed communication approach; we conclude by outlining a research agenda to empirically test the framework and examine other dynamics of extreme weather-related dialogue.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it