FutureCoast: A Playful Way to Assess Public Perceptions for Better Climate Change Communication
Bibliographic record
Abstract
We examine how the FutureCoast storytelling game creates an accessible, online space to explore the climate problem and its impacts, as well as to glean insights regarding player perceptions. Through FutureCoast, players imagine a climate-changed future by creating stories about an altered world. A total of 251 voicemail responses generated from game participants recruited through social media and other channels were coded and analyzed. Subject engagement with the storytelling game provided valuable data about climate change understanding, as well as rich, player-created narratives that document the complexity of public thinking about climate-changed futures. Commonly occurring themes include Adaptation, Challenge, Technology, Weather, Governance and Policy, and Food. FutureCoast participants perceived optimistic scenarios for technology, energy and mitigation, and pessimistic scenarios for weather, food, water and adaptation. From FutureCoast stories, we gain an understanding of public perceptions toward climate issues that can help communicators develop more informed and effective climate change communication strategies. Key policy highlightsThrough playful approaches, such as FutureCoast, we can gain an understanding of public perceptions toward climate issues that can help communicators develop more informed and effective climate change communication strategies.Using novel approaches such as games to understand perceptions can elicit information from people who would otherwise not engage in surveys or other research methods.An innovation of the FutureCoast approach is its ability to produce rich, player-created narratives, which can be analyzed to uncover complex thinking about climate-changed futures. Responses may reveal where the public identifies and voices emerging issues earlier than experts.Identifying optimistic and pessimistic trends around climate issues gives communicators the opportunity to re-frame negative climate perceptions toward actions and solutions, thus empowering their audiences with information that can elicit climate action.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".