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Record W2971837423 · doi:10.31542/j.muse.306

Where Patriarchy, Gender, and Verdicts Collide: Servant Theft Against their Masters in England during the Late Seventeenth and Early Eighteenth Centuries

2016· article· en· W2971837423 on OpenAlexaffvenue
Sarah Letawsky

Bibliographic record

VenueMacEwan University Student eJournal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsServantPatriarchyPunishment (psychology)CommitLawSociologyCriminologyPolitical sciencePsychologySocial psychologyEngineering

Abstract

fetched live from OpenAlex

Servant theft against their masters during the late seventeenth and early eighteenth centuries was common and influenced by societal expectations and regulations. Strict guidelines dictated prescriptive notions of servant behaviour, which could be difficult for servants to maintain. With limited freedom under a system of service influenced by patriarchy and religion, servants chose to commit theft offences to provide a solution to their circumstances. Male and female servants usually stole items that corresponded to their occupational roles. By examining fifteen court cases tried at The Old Bailey, one can see that male and female servants demonstrated similar levels of criminality, but female servants often received harsher punishment for their thefts than male servants due to the patriarchal framework of early modern English sociaty.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.172
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0090.011
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.017
GPT teacher head0.182
Teacher spread0.165 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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