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Record W3108930826 · doi:10.3366/anh.2020.0656

The limits of imperial influence: John James Audubon in British North America

2020· article· en· W3108930826 on OpenAlexaffabout
Debra Lindsay

Bibliographic record

VenueArchives of Natural History · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAristocracy (class)Natural historyEconomic historyArt historyArchaeologyGeographyHistoryEcologyPolitical scienceLawBiologyPolitics

Abstract

fetched live from OpenAlex

For two decades, John James Audubon (1785–1851) travelled widely and frequently while working on his illustrated natural history volumes – still highly prized today for their aesthetic and scientific merit: Birds of America (1827–1838) and Viviparous Quadrupeds of North America (1846–1854). Neither independently wealthy nor employed as a salaried scientist, the artist-naturalist with a flair for marketing financed his projects by selling subscriptions. Successfully marketing Birds to members of the British aristocracy, as well as to organizations and to artistic and intellectual elites, Audubon was reluctant to take Quadrupeds to Britain even though sales there were key to the financial viability of his work. Instead, in 1842 Audubon travelled to Canada (now Ontario and Quebec), the most populous region of British North America. The colony was, he calculated, a viable source of subscribers; however, he was wrong. Moreover, having travelled to British North America previously, he should have expected modest returns. Nonetheless, he was optimistic that this expedition would succeed where those to New Brunswick (1832) and Labrador and Newfoundland (1833) had failed. This paper examines why success eluded Audubon in the colonies, arguing that entrepreneurialism buttressed by patronage – a winning strategy in Britain – failed because there was a vast difference between metropolis and hinterland when it came to supporting the arts and sciences.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.999

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 studies0.0000.003
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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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
GenreEmpirical

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
Published2020
Admission routes2
Has abstractyes

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