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A Body Politic

2021· book-chapter· en· W3204155809 on OpenAlexaboutno aff
Brad A. Jones

Bibliographic record

VenueCornell University Press eBooks · 2021
Typebook-chapter
Languageen
FieldArts and Humanities
TopicScottish History and National Identity
Canadian institutionsnot available
Fundersnot available
KeywordsBody politicEmpirePoliticsNewspaperBritishnessAppealHistoryProtestantismRhetoricLoyaltyMedia studiesLawSociologyPolitical scienceAncient history

Abstract

fetched live from OpenAlex

This chapter discusses how newspapers helped to integrate the vast British Atlantic. Reports and editorials appearing in the dozens of “Weekly Mercuries” carried by ships crisscrossing the ocean gave meaning to an emerging, shared understanding of loyalty and loyalism among the ocean's many and varied British inhabitants. This shared understanding of Britishness drew on Protestant subjects' deeply held fears of their nation's long-standing Catholic enemies, France and Spain. The makings of Daniel Fowle's “Body Politic,” however, depended on reliable Atlantic communication networks, a circulatory system capable of carrying news quickly and regularly to all corners of Britain's vast empire. News of national importance was filtered through these more immediate webs of contact, which played a significant role in shaping distinctive local political cultures and identities in places like New York City, Glasgow, Halifax, and Kingston. During the many wars fought against France and Spain in the first half of the eighteenth century, anti-Catholic rhetoric was able to overshadow divisions within the empire, providing a language of national unity that was so intentionally broad as to appeal to the nation's diverse inhabitants. But in the absence of these wars and these enemies, as was the case for much of the 1760s and 1770s, subjects in these communities struggled to understand what exactly united them as Britons.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.181
Teacher spread0.115 · 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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