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Record W2272185722 · doi:10.1215/00982601-3337911

“Set His Image in Motion:” John Dennis and Early Eighteenth-Century Motion Imagery

2015· article· en· W2272185722 on OpenAlexaff
Sara Landreth

Bibliographic record

VenueEighteenth-Century Life · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBeautyMotion (physics)SoulPoetryEnlightenmentRhetorical questionRepresentation (politics)AestheticsNewton's laws of motionLiteraturePhilosophyArtEpistemologyLawComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This article examines early eighteenth-century attempts to explain how poetic descriptions of physical motion generated vivid scenes in the imagination. Motion was central to neo-Augustan theories of representation because, for much of this period, writers understood motion as a central principle of human nature. Questions about the mind/soul's “agitations” were crucial to a wide range of Enlightenment theories about mental imagery, rhetorical persuasion, and aesthetic beauty. Many writers, including John Dennis, linked the aesthetic import of spatial movement to Newtonian laws of motion. I trace the ways that Dennis's theories about motion imagery address not only the classical tradition of “bringing-before-the-eyes,” but also the modern philosophical models of inertia and force. In arguing that writing about poetry could better elucidate the motions of the mind/soul than could moral philosophy or physics, Dennis's essays can also teach us about eighteenth-century proto-disciplinary organization. Recent work in cognitive cultural studies has shown that the concept of motion is of vital importance to understanding the way the brain comprehends and structures language. This has especially wide-reaching implications for scholars of Restoration and eighteenth-century literature because it was during this period that writers began to codify connections between universal laws of nature, beauty, and human cognition.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.024
GPT teacher head0.274
Teacher spread0.249 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

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