Code red for humanity or time for broad collective action? Exploring the role of positive and negative messaging in (de)motivating climate action
Bibliographic record
Abstract
Despite decades of warning from climate scientists, the international community has largely failed at reining in planet-warming greenhouse gas (GHG) emissions. In this context, scientific assessments of climate change—like those periodic reviews provided by the Intergovernmental Panel on Climate Change (IPCC)—are repeatedly faced with the challenge of communicating the rapidly closing window for securing a livable future on Earth. Yet, it remains unclear whether sounding “code red for humanity” fosters climate action or climate paralysis. The ongoing debate among climate change communication scholars about the (in)effectiveness of fear-based messaging sheds light on three intertwined and often overlooked aspects of emotional appeals in communication: the content of the message frame, the emotional arousal it induces, and the values and dispositions of the audiences receiving the message. While previous work has addressed questions related to one or two of these aspects, this study examines the role of positive and negative messaging in (de)motivating climate action, with particular attention to how messages, emotions and audiences interact in the process of communication. Leveraging data drawn from a sample of environmental group supporters in Canada (N = 308), we first identify and describe four unique audiences within supporters of Canada's environmental movement that vary in their levels of engagement and radicalism. We then examine how negative and positive messaging influence emotional arousal and climate action across audience segments. We find that negative messages about climate change (e.g., sounding “code red for humanity”) can be less mobilizing than positive messaging, even when the message is directed toward relatively engaged audiences and followed by the opportunity to take a specific, actionable and effective action. These findings help shed light on the potential limits of fear-based messaging in the context of a global public health pandemic while further highlighting the importance of communicating in ways that inspire people through hopeful and optimistic messages.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".