Shimmering in the Swamp: Wetlands, Danger, and Ecological Refractions in<i>Annihilation</i>
Bibliographic record
Abstract
Swamps have long been a fruitful setting for horror films. In Creature from the Black Lagoon (1954), Frogs (1972), and The Bay (2012), swamps birth monsters, from giant snakes to microscopic flesh-eating parasites. In part, the swamp as fertile horror setting is a product of the complicated and shifting views North American settlers have held towards wetlands, whether as obstacle and diseased, biodiverse and ecologically productive, and in need of both destruction and conservation. These layered divergent views have refigured wetlands as contradictory spaces where the monstrous can hide. In this article, I explore the 2018 science-fiction horror film Annihilation, Alex Garland’s adaptation of Jeff VanderMeer’s 2014 novel of the same name, to demonstrate how the film deploys the wetland setting as a fluid space which fosters posthumanist entanglements. I focus solely on the film adaptation as opposed to the novel both because I want to situate the film...
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".