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Record W2117156693 · doi:10.1017/s0738248009990058

Felons' Effects and the Effects of Felony in Nineteenth-Century England

2010· article· en· W2117156693 on OpenAlexaffabout
K. J. Kesselring

Bibliographic record

VenueLaw and History Review · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsQuarter (Canadian coin)SurpriseCreaturesLawIndictmentFreeholdPolitical scienceHistoryCriminologySociologyArchaeologyNatural (archaeology)

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.805

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0220.026
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.010
GPT teacher head0.186
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2010
Admission routes2
Has abstractyes

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Same venueLaw and History ReviewSame topicHistorical Economic and Social StudiesFrench-language works237,207