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Oracles of Empire: Poetry, Politics, and Commerce in British America, 1690-1750

2017· dataset· en· W2329960170 on OpenAlexaboutno aff
William L. Sachse

Bibliographic record

VenueThe SHAFR Guide Online · 2017
Typedataset
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsEmpirePoetryMoralityPoliticsHistoryCivilizationPrint cultureBritish EmpireMercantilismPeriod (music)LawLiteratureClassicsAncient historyArtArt historyPolitical scienceAestheticsArchaeology

Abstract

fetched live from OpenAlex

This innovative look at previously neglected poetry in British America represents a major contribution to our understanding of early American culture. Spanning the period from the Glorious Revolution (1690) to the end of King George's War (1750), this study critically reconstitutes the literature of empire in the thirteen colonies, Canada, and the West Indies by investigating over 300 texts in mixed print and manuscript sources, including poems in pamphlets and newspapers. British America's poetry of empire was dominated by three issues: mercantilism's promise that civilization and wealth would be transmitted from London to the provinces; the debate over the extent of metropolitan prerogatives in law and commerce when they obtruded upon provincial rights and interests; and the argument that Britain's imperium pelagi was an ethical empire, because it depended upon the morality of trade, while the empires of Spain and France were immoral empires because they were grounded upon conquest. In discussing these issues, Shields provides a virtual anthology of poems long lost to students of American literature.

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)0.001
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.359
Threshold uncertainty score0.714

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.024
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.011

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 head0.035
GPT teacher head0.381
Teacher spread0.346 · 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
GenreDataset

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

Citations60
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe SHAFR Guide OnlineSame topicAmerican Constitutional Law and PoliticsFrench-language works237,207