Felons' Effects and the Effects of Felony in Nineteenth-Century England
Bibliographic record
Abstract
On May 17, 1853, a court sentenced Francis Prout of East Stonehouse, Devon, to six months' hard labor for receiving £1 15s. in stolen money. Prout's “lodger,” a Mary Ann Foss, had stood charged with the theft at the local quarter sessions, but during her trial she denounced Prout as a brothel keeper who profited from crimes committed in his house. With no real warning, Prout found himself tried and convicted. An even more alarming surprise followed a few days later, when the local authorities decided to pursue Prout's property. They invoked the ancient practice by which felons forfeited their possessions, claiming not just Prout's moveable goods, as was common, but also his ninety-nine-year leases on two local pubs and the profits from his freehold on a pub and houses in Plymouth. The latter constituted an unusual decision, in part because the inquisition necessary to seize the property would cost about £150, and in this case no interested party stepped forward to pay the fees. But as the chairman of the quarter sessions argued, Prout's property was “chiefly acquired by the wages of prostitution.” Underneath his talk of “fallen women” and “unfortunate creatures” lay a very modern concern with the illicit proceeds of criminal activity. In such a case, the chairman opined, the property in question should be forfeit.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".