The Post-Apocalyptic Imaginary: Science, Fiction, and the Death Drive
Bibliographic record
Abstract
This article considers why the relatively new genre of post-apocalyptic fiction is proliferating in the twenty-first century and argues that its compulsive return to scenes of the destruction of the world is symptomatic of a traumatized culture. The violence of World War I– where the co-option of science by the military and state in the name of civilization made possible industrial-scale killing – persists in haunting the collective psyche even as, on a ‘rational’ level, we continue to invest in the contrary hope that this technology is future-orientated and will solve the problems of the world. The latest concerns around artificial intelligence and autonomous killer robots highlight the ways in which what Freud referred to as ‘the death drive’ and ‘the destructive impulse’ govern the logic of both our anxiety about and celebration of this technology. The second part of the article reviews the complicated relationship between science and fiction, where on the one hand science uses fiction to promote its inventions and on the other warns that the fears fuelled by the ‘hype’ of fiction hinder its progress and thus must be exiled from ‘serious’ discussions of science. I conclude that, on the contrary, science needs fiction to assist in restoring an ethical impulse to this technology.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".