Climate Change Imaginaries? Examining Expectation Narratives in Cli-Fi Novels
Bibliographic record
Abstract
A new generation of climate fiction called Cli-fi has emerged in the last decade, marking the strong consensus that has emerged over climate change. Science fiction’s concept of cognitive estrangement that combines a rational imperative to understand while focusing on something different from our everyday world provides one linkage between climate fiction and science fiction. Five novels representing this genre that has substantial connections with science fiction are analyzed, focusing on themes common across these books: their framing of the climate change problem, their representations of science and scientists, their portrayals of economic and environmental challenges, and their scenarios for addressing the climate challenge. The analysis is framed through Taylor’s ideas of the social imaginary and the sociology of expectations, which proposes that expectations are promissory, deterministic, and performative. The novels illustrate in varying ways the problems attending the science-society relationship, the economic imperatives that have driven the characters’ choices, and the contradictory impulses that define our connections with nature. Such representations provide a picture of the challenges that need to be understood, but scenarios that offer possibilities for change are not as fully developed. This suggests that these books may represent a given moment in the longer trajectory of climate fiction while offering the initial building blocks to reconsider our ways of living so that new expectations and imaginaries can be debated and reconceived.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".