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Record W2562141387 · doi:10.1215/00982601-3695963

Old &amp; New <i>Foundling Hospitals for Wit</i> in the Age of the Digital Miscellanies Index

2017· article· en· W2562141387 on OpenAlexaff
Donald W. Nichol

Bibliographic record

VenueEighteenth-Century Life · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMiscellanyPoetryOrnamentsReignArtOrder (exchange)LiteratureHistoryArt historyPoliticsLaw

Abstract

fetched live from OpenAlex

Literary miscellanies have long been popular. Richard Tottel's 1557 collection, Songes and Sonettes, gathered the works of various Tudor poets. In 1684, Jacob Tonson and John Dryden launched a miscellany that reached six volumes by 1709. By 1743, the time was ripe for a satirical miscellany; Sir Robert Walpole's reign as Britain's prime minister had just come to an end, and Pope revised his Dunciad to devastating effect. Out of the mix of political uncertainty and satiric excess emerged The Foundling Hospital for Wit, which ran to six volumes by 1749. It offered a potpourri of parodies set to old tunes and prose extracts haranguing the new administration and promoting amputation with Swiftian gusto. One of the problems facing twenty-first-century readers is how to get a handle on what exactly is being satirized. In order to gain a fuller understanding of how this collection and its sequel, The New Foundling Hospital for Wit (1768-73), came about, we need to probe its printers, booksellers, and editors, whose names, more often than not, are omitted or disguised. Ornaments provide one key to determining who printed what. The Foundling Hospital for Wit appears to have been the brainchild of Sir Charles Hanbury Williams. The New Foundling Hospital for Wit starts off with his poem, “Isabella,” which stands up well beside Rape of the Lock. A vehicle for John Wilkes and his radical bookseller John Almon, this later miscellany offered up the most audacious satires and politicized engravings of its time.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score0.875

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 studies0.0010.001
Scholarly communication0.0010.000
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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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