Combining Data Visualization and Interactive Narrative: A Persuasive Approach to Raise Climate Change Awareness
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
Climate change is a global phenomenon that affects every living being on our planet. Raising awareness among people about climate change and helping them realize the possible consequences of their actions is key to mitigating climate change problems. Our research was aimed at achieving this by building a persuasive intervention that combines visualization of climate change data and an interactive narrative that demonstrates how our actions can impact the climate. We conducted a user study with 100 participants and found evidence showing that our system was effective in significantly promoting behavioral intention to mitigate climate change. We found defensive responses as a key factor that is negatively influencing the effect of our intervention on the participants. Compelling visuals and multiple interaction options, simulating climate actions and their consequences, and reducing the effort to learn about the phenomenon were significant positive techniques used in the intervention. Additionally, the social elements of our intervention played a major role in promoting participants’ willingness to perform proenvironmental behavior. Our work contributes to the field of persuasive technology, data visualization, interactive narratives, and climate research by introducing a new persuasive way of communicating climate change information to the public using a combination of data visualizations and interactive narratives.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it