Zombies, Superpowers, and Apocalyptic Narratives: Exploring Science Fiction as a Messy Space for Public Engagement on Radiation, Nuclear Power, and Solving Climate Change
Bibliographic record
Abstract
This paper presents a review of the literature on apocalyptic narratives and a review of science fiction methods of public engagement to investigate novel approaches to increase youth participation in climate change and nuclear energy discussions. The literature on apocalyptic narratives in climate change discourse is reviewed, and evidence is gathered through a review of science fiction stories about radiation, including detailed analysis of four mainstream films: Godzilla, the Hulk, Spider-Man, and Night of the Living Dead. Both the original film debuts and more recent remakes of each story are reviewed. In addition, a review of the intersection of science fiction and public engagement methods is presented and analyzed to explore how sci fi storytelling could be used to engage with younger people regarding solving climate change in ways that increase diversity of thought, rather than perpetuate policy procrastination. Apocalyptic narratives are common throughout climate change discourse and are prevalent in concerns expressed over nuclear technologies. Furthermore, fictional stories regarding the end of the world are increasingly popular in the sci fi genre of popular culture. The climate change crisis is a real apocalyptic story rather than a fictional one, which makes finding solutions more difficult. Multiple perspectives, including polarized opinions, anxiety, apathy, and disbelief all compete for the attention of policy makers and create chaotic circumstances for public participation in problem solving. Nonfictional apocalyptic narratives are often used in climate change discourse to sound alarms over the need for urgent policy action. Although thinking about real apocalyptic problems may worsen anxiety or reinforce previously held positions, science fiction stories can open up perspectives while increasing interest in learning more through fun and entertaining topics. This paper presents recommendations on how novel, science fiction-based methods of public engagement could be utilized to increase interest among younger people in participating in real conversations regarding climate change and nuclear power. Recommended approaches are identified using the dynamic split ladder of participation framework, and limitations and considerations surrounding gender diversity are outlined as a future area of research on science fiction methods of public engagement.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".