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Record W3046256170 · doi:10.1177/08438714211037685

‘Arbitrary and cruel punishments’: Trends in Royal Navy courts martial, 1860–1869

2021· article· en· W3046256170 on OpenAlexaff
Andrew M. Johnston

Bibliographic record

VenueInternational Journal of Maritime History · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsNavyMinor (academic)LawMartial lawMilitary justiceLegislaturePolitical scienceCorporate governancePunishment (psychology)HistoryManagementPsychologyPoliticsEconomics

Abstract

fetched live from OpenAlex

Despite minor amendments to the Royal Navy's Articles of War throughout the eighteenth century, and a major reworking in 1749, both capital and corporal punishments were frequently employed as punishment for minor offences in a system that made England's ‘Bloody Code’ look positively humane. The 1860 Naval Discipline Act provided the first substantive overhaul of the original Articles of War, but historians have generally lamented this act as providing little comprehensive change to the governance of the navy. Using statistical data collected from thousands of courts martial records, this article takes a broad look at trends in naval courts martial, studying how these courts interacted with the legislative changes of the 1860s. Viewing how charges and sentences altered on the global scale, it becomes clear that the ‘arbitrary and cruel punishments’ of the previous century had at last given way to a centralized, formal expression of discipline.

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)0.001
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.221
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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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