MétaCan
Menu
Back to cohort
Record W1989107763 · doi:10.1017/s0018246x13000459

PARLIAMENT AND SOME ROOTS OF WHISTLE BLOWING DURING THE NINE YEARS WAR

2014· article· en· W1989107763 on OpenAlexaff
Matthew Neufeld

Bibliographic record

VenueThe Historical Journal · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsParliamentHouse of CommonsWrongdoingNavyLawState (computer science)Select committeeLanguage changeHistoryGovernment (linguistics)CensorshipCommonsPolitical scienceGeorge (robot)PoliticsLiteratureArtPhilosophyArt history

Abstract

fetched live from OpenAlex

ABSTRACT This article argues that the failed campaign of one former clerk against corruption in the Royal Navy's sick and wounded service during the Nine Years War sheds light on some roots of modern whistle blowing. During the 1690s, England's parliament took important steps towards becoming an organ of inquiry into the workings of all government departments. Parliament's desire for information that could assist it to check Leviathan's actions, coupled with the end of pre-publication censorship in 1695, encouraged the advent of pamphleteering aimed at showing how to improve or correct abuses within the administrative structure and practices of the expanding fiscal-military state. It was from this stream of informative petitioning directed at the Commons and the Lords that informants such as Samuel Baston, as well as George Everett, William Hodges, and Robert Crosfeild, tried to call time on either systematic injustices or particular irregularities within the naval service for what they claimed was the public interest. What they and others called ‘discovering’ governmental malfeasance should be seen as early examples of blowing the whistle on wrongdoing.

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)0.013
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.017
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0040.006
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 head0.020
GPT teacher head0.188
Teacher spread0.167 · 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
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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Historical JournalSame topicHistorical Economic and Social StudiesFrench-language works237,207