Out of This World: Embodying Uncanny Precarity in Amitav Ghosh’s Speculative Intertext on Global Warming
Bibliographic record
Abstract
Abstract Climate science has been one essential aspect of generating consensus that global warming is happening, is caused by human actions, and is harmful to biodiversity and the habitability of Earth for the human species. Climate action epistemologies draw on climate science to trace the origins of global warming to the Industrial Revolution. In contrast, the diverse epistemologies of the transnational climate justice movement take as a starting point the centuries of anti-Indigenous and anti-Black colonial dispossession, extraction, and enslavement through which global warming has emerged. Decolonial speculative fiction writers name systemic injustices of patriarchal colonial impunity through worldbuilding that foregrounds characters creating relationships of accountability and care on planet Earth: out of this world. This article focuses on how Amitav Ghosh’s speculative intertext of the novel Gun Island and non-fiction books The Great Derangement: Climate Change and the Unthinkable and The Nutmeg’s Curse: Parables for a Planet in Crisis disrupts the neoliberal rationale of international climate action frameworks that limit accountability to pursuing state transparency through goal setting to mitigate the harms of global warming. I propose that a decolonial feminist reading of aesthetic invocations of the transnational uncanny that stage encounters with the agency of the more-than-human world is a method of reassembling the systematically repressed knowledge of colonial extraction and hierarchies of precarity through which the planetary condition of global warming has emerged.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".