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Record W7135010660 · doi:10.1353/vcr.2025.a985007

Coal and Anti-Blackness in Thomas Hood's "The Demon-Ship"

2025· article· en· W7135010660 on OpenAlexvenueno aff
Kent Linthicum

Bibliographic record

VenueVictorian review · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsCoalPoetryMateriality (auditing)Fossil fuelCoal miningHearth

Abstract

fetched live from OpenAlex

Abstract: The long associations between coal and Blackness in British culture changed in the nineteenth century. No longer was coal merely fuel for the hearth fire or an ingredient in proto-industrial manufacturing; it powered engines in factories, steam ships, and trains. In conjunction with the regime of transatlantic slavery, Blackness and coal developed new associations in the British imaginary. These associations helped engender an anti-Black coal culture. This essay analyzes one of these associations—"dirtiness"—in Thomas Hood's comedic poem "The Demon-Ship" (1827). In the poem, the filth of coal turns a crew of collier sailors into enslaved Black people, which sets up the poem's punchline. Here the materiality of coal is used to extend and reinforce anti-Blackness. "The Demon-Ship," despite depicting fossil fuel infrastructure, makes multiple references to transatlantic slavery. These references and the overall usage of Blackness in the poem are nominally humorous. The poem's humour and its participation in anti-Black coal culture only serves to weaken transatlantic solidarity. Rather than seeing links between abolition and workers' rights, the poem gives a British audience a group to look down on. In this way, coal was an ingredient in the anti-Black climate of the nineteenth century. Recovering this relationship between fossil fuels and race is essential for understanding the Victorian period and the nature of energy transitions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.230
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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