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Record W4414747628 · doi:10.1017/s0738248025101302

Conspiracy, Crime, and Conflict in the Court of Star Chamber

2025· article· en· W4414747628 on OpenAlexafffund
K. J. Kesselring

Bibliographic record

VenueLaw and History Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoliticsState (computer science)CovertCivil libertiesNarrativeQueen (butterfly)

Abstract

fetched live from OpenAlex

To those living through them, the Elizabethan and early Stuart years of England’s history seemed unusually riven by plots and conspiracies. Protestants feared the public effects of the private machinations of the Scottish queen and her supporters, of Jesuits, and of perfidious “papists” more generally. Catholic polemicists countered with narratives of dark deeds done by men who subverted rather than served the Crown: “secret histories” circulated that warned of William and Robert Cecil, the earl of Leicester, and others undermining the public state of the realm. 1 Very real conspiracies by men such as the Earl of Essex and Guy Fawkes fostered fears of others. From the hard and hungry 1590s, protests against enclosures and lack of food became so common and concerning that the authorities contrived to brand some such riots as the products of treasonous conspiracies that threatened not just particular landlords or grain merchants but the public at large. 2 Over the early seventeenth century, fears of covert machinations by both the poor and the powerful only increased, culminating in the fear that King Charles himself had become a pawn in a Catholic conspiracy that endangered the lives and liberties of his subjects. 3 Talk of plots and conspiracies—real and imagined—abounded in an increasingly divided and discordant political culture, seen as threatening a “public” they arguably helped to create.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.114
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0500.035
Scholarly communication0.0160.005
Open science0.0010.008
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0090.001

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 head0.045
GPT teacher head0.326
Teacher spread0.281 · 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
Published2025
Admission routes2
Has abstractyes

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