Bibliographic record
Abstract
Single-sheet, cheap-to-print publications popularized in urban centres in the seventeenth and eighteenth centuries, broadsides were used to disseminate knowledge and entertainment among the common reading classes of England. Murder broadsides were a particularly common genre during the Victorian period in the mid-to-late-nineteenth century, and while the majority of these focused on the downfalls of male criminals, a significant number of publications concerning female murderers survives. Studying how broadsides were produced, read, and shared, especially in London, this paper examines the significance of their representations of female murderers. Ultimately, murder broadsides about women embodied the anxieties of the Victorian age. In the city, social boundaries were pushed, crossed, and blurred relentlessly; broadsides and their representations of women were active expressions of and responses to such anxieties. But while the tendency of broadsides was towards “moral conservativism” which sought to condemn violent crime, promote sexual purity, and sanctify chaste mothers and wives, broadsides also afforded the women they portrayed a certain notoriety and voice, complicating the common reading of the broadside as a prescriptive or even repressive document.
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 | 0.002 |
| 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 | 0.022 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".