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Record W3178177680 · doi:10.82308/25126

Musicians and intelligence operations, 1570-1612: politics, surveillance, and patronage in the late Tudor and early Stuart years

2007· article· en· W3178177680 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueeScholarship@McGill (McGill) · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies of British Isles
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPoliticsPolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

The problem of musicians’ involvement in intelligence operations during the late Tudor and early Stuart years has to date remained relatively unexplored. There is convincing evidence, however, that artists from different disciplines were particularly targeted for recruitment in intelligence operations, designed by Elizabeth I’s councillors, Willam Cecil, Lord Burghley and Francis Walsingham, to infiltrate and disable Catholic oppositional networks on the Continent and in England in the aftermath of the Elizabethan settlement on religion. The Scottish revolt that preceded the arrival of Mary, Queen of Scots in England (1568), the Northern Rising of Catholic Earls (end of 1569), the excommunication of Elizabeth I (1570), and the so-called “Ridolfi” plot to assassinate Elizabeth and raise the Queen of Scots to the English throne (uncovered in 1571) combined to create a large-scale political crisis that galvanized the fledgling intelligence operations, dubbed by scholars as the first “modern” secret service. Religious and political upheavals in late Tudor England had marked consequences on artistic patronage. Although this dissertation is not a comprehensive study of music patronage as it shifted with changing networks of power, I will propose that a form of alternative patronage did emerge with the growth industry in intelligence operations. By the 1580s, large numbers of university students and artists, among them the great Eizabethan dramatist Christopher Marlowe, were recruited to serve in the covert war that mirrored mounting overt hostilities in the Netherlands and in France. By the 1590s, after Walsingham’s death, the Earl of Essex created his own intelligence service, which gradually became an instrument of Essexian aspiration to royal favour. Robert Cecil, Burghley

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.215
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