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Record W4387558304 · doi:10.1017/9781009392129.004

Changing Cultures of Execution: Reason and Reforms, 1770–1808

2023· book-chapter· en· W4387558304 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRedressCrowdsPrisonSensibilityNewspaperHistoryCurranLawPolitical scienceSociologyMedia studiesComputer security

Abstract

fetched live from OpenAlex

As soon as newspapers, catering to England’s new urbane peoples, began describing common executions, the crowds attending them were seen as indifferent to their moral message. By the middle of the eighteenth century, execution rituals seemed equally problematic. Critics perceived hangings to be so frequent, so large-scale and so brutalizing to an even minimally refined sensibility as to defeat their deterrent purpose. In 1783, London officials sought to redress these problems by devising a new execution ritual, staged immediately outside the prison and courthouse. Within four decades, this quintessentially urban execution ritual had been adopted in almost all other English counties, even as cities on the continent pointedly moved executions outside urban centres. Yet still executions seemed ineffective. Following a particularly intense crisis in the 1780s, England’s traditional ruling elites sought to preserve the “Bloody Code” by reducing the scale of hangings to historically low levels.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.038
Scholarly communication0.0070.003
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.257
Teacher spread0.217 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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