An Empire of Red Weed: Environmental Infrastructure in H. G. Wells's <i>The War of the Worlds</i>
Bibliographic record
Abstract
This article argues that natural environments should be seen as life-sustaining infrastructures. Countering a tendency to see infrastructures as human-engineered, I show how environmental infrastructures are shaped by humans and nonhumans alike, with their life-sustaining roles often only revealed by their disruption. I demonstrate how infrastructures might be shaped by nonhumans by highlighting the career of Elodea canadensis, or Canadian waterweed, an introduced plant that wrought havoc on Britain's watery environments in the mid- to late nineteenth century. Red weed, the plant introduced by the Martians in H. G. Wells's The War of the Worlds, echoes Elodea in its course through the British environment. But while Elodea and similar introduced species prompted nascent critiques of imperial plant movement, I show how Wells avoids such critiques by deleting red weed, and any further environmental consequences following from its introduction, from his novel's end. Instead, Wells endorses a natural-selection-driven explanation for the British environment's superior fitness—an explanation that affirms rather than critiques ecological imperialism and mitigates the role of nonhumans in reshaping environmental infrastructures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".