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Record W4392319310 · doi:10.1515/9781787445185

State Surveillance, Political Policing and Counter-Terrorism in Britain

2021· book· en· W4392319310 on OpenAlexaboutno aff
Vlad Solomon

Bibliographic record

VenueBoydell and Brewer eBooks · 2021
Typebook
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)TerrorismCounter terrorismPolitical sciencePoliticsCriminologyLawComputer securitySociologyComputer science

Abstract

fetched live from OpenAlex

Examines the formation of state surveillance and the emergence of institutionalized political policing in late Victorian and Edwardian Britain. This book deals with the formation of state surveillance and the emergence of institutionalized political policing in late Victorian and Edwardian Britain. Little has been written on this early formative period for the British security state, which began in earnest as a response to the Fenian dynamite campaign of the 1880s. Based on newly declassified documents, Solomon weaves together separate narrative threads which converge to paint a complex picture ofthe institutional innovations and personal rivalries that produced Britain's first national political police. The interactions between high-ranking bureaucrats, policemen and politicians reveal how often conflicting ideas on controlling organized radicalism coalesced into a unified counter-subversive strategy. Stressing the distinctness of the early British model of political policing, the narrative goes past the confines of a scholarly account by using source material to flesh out multidimensional characters, ranging from choleric Home Secretaries to remorseful anarchist double agents embroiled in a high-stakes and often unscrupulous combination of espionage, collusion and betrayal. VLAD SOLOMON is an independent scholar living in Montreal, Canada. He holds a PhD in history from McGill University.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.278
Teacher spread0.264 · 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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

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