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Record W2586787109 · doi:10.1093/ahr/122.1.245

Elizabeth Yale. Sociable Knowledge: Natural History and the Nation in Early Modern Britain.

2017· article· en· W2586787109 on OpenAlexaff
Brian Cowan

Bibliographic record

VenueThe American Historical Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsNatural historyNatural (archaeology)HistoryArchaeologyBiologyEcology

Abstract

fetched live from OpenAlex

The recent Brexit vote in the UK has brought the question of national identity in the British Isles back to the forefront of both popular and scholarly attention. Should Britain be considered part of Europe or a distinct entity with a rather different culture and a distinct history? To what degree are the histories of England, Scotland, and Ireland part of the same national story? Elizabeth Yale’s Sociable Knowledge: Natural History and the Nation in Early Modern Britain does not address these questions directly, but they lurk in the shadows of a work that focuses on the difficult scholarly labors of seventeenth-century natural historians in Britain and Ireland. The book uses the insights of historians of print culture and the new social history of knowledge to develop a new perspective on how natural historians managed to write their works, to communicate their knowledge, and ultimately to imagine how their histories fit into a larger history of the British nation. The irony behind this story, as true today as it was in the seventeenth century, is that despite the intensity of their devotion, these labors never cohered. Natural history and the history of the nation in early modern Britain remained incomplete and fragmented projects.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.258
Teacher spread0.218 · 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
GenreReview

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

Citations1
Published2017
Admission routes1
Has abstractyes

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