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The Southern Origins of Bohemian New York

2017· book-chapter· en· W2884118853 on OpenAlexaboutno aff
Edward Whitley

Bibliographic record

VenueUniversity of North Carolina Press eBooks · 2017
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalonQuarter (Canadian coin)HistoryArt historyPortraitQueen (butterfly)ArtArchaeology

Abstract

fetched live from OpenAlex

The first Americans to identify as artistic bohemians gathered at a Manhattan beer cellar in the 1850s. They counted Walt Whitman as one of their number, and considered Edgar Allan Poe a bohemian <italic>avant la letter.</italic> But New York’s first bohemians were not displaced Parisians living in a section of the Latin Quarter magically transplanted to the United States. Rather, bohemianism in the United States has roots in Charleston, South Carolina, the hometown of both Ada Clare (the “Queen of Bohemia” and host of a weekly literary salon) and Edward Howland (the financial backer for the bohemians’ literary weekly, <italic>The New York Saturday Press</italic>), as well as in the setting of Poe’s “The Gold-Bug” (1843), which influenced the first literary representation of American bohemianism in Fitz-James O’Brien’s short story “The Bohemian” (1855). Charleston’s cotton plantations provided Howland and Clare with the money to fund the institutions that were essential for bohemianism to flourish: the periodical and the salon. With Poe at the imaginative center of American bohemia and Clare and Howland at its financial center, U.S. bohemianism emerges as a complex network of people, money, and ideas circulating between the North and the South as well as New York and Paris.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.954
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.053
GPT teacher head0.191
Teacher spread0.138 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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