Bibliographic record
Abstract
Despite the occasional upsurge of climate change scepticism among Anglophone conservative politicians and journalists, there is still a near consensus among climate scientists that current levels of atmospheric greenhouse gas are sufficient to alter global weather patterns to disastrous effect. The resultant climate crisis is simultaneously both a natural and a socio-cultural phenomenon and in this book Milner and Burgmann argue that science fiction occupies a critical location within this nature/culture nexus. Science Fiction and Climate Change takes as its subject matter what Daniel Bloom famously dubbed ‘cli-fi’. It does not, however, attempt to impose a prescriptively environmentalist aesthetic on this sub-genre. Rather, it seeks to explain how a genre defined in relation to science finds itself obliged to produce fictional responses to the problems actually thrown up by contemporary scientific research. Milner and Burgmann adopt a historically and geographically comparatist framework, analysing print and audio-visual texts drawn from a number of different contexts, especially Australia, Britain, Canada, China, Finland, France, Germany, Japan and the United States. Inspired by Raymond Williams’s cultural materialism, Pierre Bourdieu’s sociology of culture and Franco Moretti’s version of world systems theory, the book builds on Milner’s own Locating Science Fiction to produce a powerfully persuasive study in the sociology of literature.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".