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Record W2566338190 · doi:10.1215/00982601-3696175

Deformity Poems and Other Nasties

2017· article· en· W2566338190 on OpenAlexaff
Simon Dickie

Bibliographic record

VenueEighteenth-Century Life · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComicsDictionPoetryLiteratureSensibilityNothingMainstreamOffensivePraiseArtHistoryPhilosophy

Abstract

fetched live from OpenAlex

This essay discusses two categories of eighteenth-century comic verse that were mainstream in their time but are now almost forgotten. They have little or nothing to do with the satiric traditions that dominate critical attention. Part 1 explores a mass of comic verse tales, direct descendants of the medieval fabliau. In the process, it explores a best-selling anthology of them, the Noble brothers' The Muse in Good Humour (1744–85), and the career of Matthew Prior, the greatest contemporary producer of this genre and by far the most prominent English poet between Dryden and Pope. While often offensive in content, eighteenth-century verse tales remain quick and colloquial, still leaping off the page after 250 years. Their easy tetrameter couplets and plain diction remind us that neoclassical strictures only stretched so far. Part 2 introduces a stranger and less-appealing set of texts: cruel poems about physical defects and disabilities. Few texts could be more alien to current norms about the eighteenth century as an Age of Sensibility. Yet these “deformity poems” were produced in vast quantities and in every conceivable form, from epigrams about squinters, to long and absurdly sentimental tales about hunchbacks in love. Both professionals and amateurs seem to have used them as technical exercises—as a way of amusing themselves and showing off their skill at mimetic versification. As such, deformity poems illustrate the everyday uses of verse in this culture: they were written by everyone, circulated in and out of print, and constantly adapted to new audiences and new occasions.

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 score0.894
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 studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.041
GPT teacher head0.299
Teacher spread0.257 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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