Bibliographic record
Abstract
While some scholars in eighteenth-century England identified a link between the smell of the dead and the spread of diseases, it was not until the nineteenth century that the dead were no longer buried in London’s inner-urban churchyards. This article draws attention to the multiplicity of voices when it came to the links between bad smells and disease and argues that whether there was a connection between the two was by no means a matter of scholarly consensus. While some hygiene reformers emphasized the possibility of the spread of diseases through the air, others opposed this view, and while theories about miasma retained some influence, they were also challenged and debated. This article therefore challenges a linear narrative of the movement of dead bodies from inner-urban cemeteries to the outskirts of towns based on fears that the dead might spread harmful vapours. It argues instead that already in the eighteenth century, prominent medical professionals questioned the connection between a pollution of the air through the dead and a spread of diseases. Even in the nineteenth century, there was no straightforward deodorization effort, but it was rather the confluence of a range of different factors, only one of which involved concerns about hygiene, that led to the relocation of burial sites. Taking the example of the English metropolis of London, this article shows that considering the smell of the dead can enhance our understanding of the functioning and planning of towns as well as the spaces of the dead more generally.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".