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Record W2739696027 · doi:10.7202/1040562ar

Murder and Mutilation in Early-Stuart England: A Case Study in Crime Reporting

2017· article· en· W2739696027 on OpenAlexfundvenueno aff
Ken MacMillan, Melissa Glass

Bibliographic record

VenueJournal of the Canadian Historical Association · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsConvictionCriminologyInterpretation (philosophy)Variety (cybernetics)George (robot)SisterEconomic JusticeHistoryCriminal justiceLawSociologyPolitical scienceArt historyPhilosophyComputer science

Abstract

fetched live from OpenAlex

Although historians have long recognized that crime pamphlet authors were not very faithful reporters, it has been difficult for them to establish precisely how much fiction this literature contained because of the limited availability of other sources with which to compare them. Using a case study approach, this essay examines two murder pamphlets, both written in 1606, that describe the murder of a young boy, Anthony James, the mutilation of his sister, Elizabeth, and the conviction and execution of their alleged assailants, Agnes and George Dell. The presence of two pamphlets describing the same series of crimes was unusual, and, through a process of detailed comparison and critical interpretation, provides us with an opportunity to reflect further on the accuracy and purpose of crime reporting in early modern England. The two versions contain a great deal of contradictory information, were seemingly written for very different audiences, served a variety of functions for contemporary readers, and raise the question of whether the authors believed that justice was done in this case.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0180.009
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.288
Teacher spread0.195 · 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 designQualitative
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

Citations2
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Historical AssociationSame topicHistorical Studies on Reproduction, Gender, Health, and Societal ChangesFrench-language works237,207