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True Crime Reporting in Early Modern England

2016· reference-entry· en· W2604094051 on OpenAlexaff
Ken MacMillan

Bibliographic record

VenueOxford Research Encyclopedia of Criminology and Criminal Justice · 2016
Typereference-entry
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBalladRepentanceMoralityCriminologyDeferenceObedienceLawAdulteryImmoralityCriminal justiceHistorySociologyPolitical scienceLiteratureArtPoetry

Abstract

fetched live from OpenAlex

Abstract True crime reporting was extremely popular in early modern England (ca. 1550–1800). Depending on when this literature was written, and the audience it was intended to attract, the sub-genres of true crime writing took the form of small pamphlets, broadsides, rhyming ballads designed to be sung to a familiar tune, ministers’ accounts of criminals and repentance, collections of trials, newspapers, and biographies of professional criminals. In addition to being inherently shocking and entertaining, this literature served as cautionary, religious, and morality tales that reflected on serious crime as one of the signs that English society had become ignorant, irreligious, and immoral. These tales of true crime could be used to remind a wide readership of the wages of sin, and of the role of the community, church, and state in bringing about justice for criminals and their victims. In a society that placed significant restraints on sexual, personal, and religious freedoms, and exhorted obedience, deference, hard work, sexual restraint, and abstinence from all forms of vice, true crime literature could help to restore order and balance to society. To accomplish these various goals, the authors of true crime literature were not very faithful reporters, often embellishing criminal deeds, publishing small portions of much lengthier cases, or fabricating facts as needed to fill in unknown details or to improve readers’ fear of and education about the criminal element that surrounded them. As this literature developed in the 18th century, its authors became famous for reporting about infamous criminals in semi-biographical novels, thus merging true crime literature with fiction and giving rise to another genre of crime literature by about 1800.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.407
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2016
Admission routes1
Has abstractyes

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