Bibliographic record
Abstract
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".