Extreme Weather Events as Teachable Moments: Catalyzing Climate Change Learning and Action Through Conversation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".