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Record W2220047837 · doi:10.3138/ecf.28.2.345

Fugitive Pieces: Language, Embodiment in Eighteenth-Century Texts

2015· article· en· W2220047837 on OpenAlexvenueno aff
Daniel DeWispelare

Bibliographic record

VenueEighteenth-Century Fiction · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAdventureOrder (exchange)CategorizationInterpersonal communicationHistoryLiteratureLinguisticsSociologyPsychologyAestheticsArtSocial scienceArt historyPhilosophy

Abstract

fetched live from OpenAlex

By attending to the diverse ways that eighteenth-century fugitive advertisements collapse together the bodily and linguistic habits of wanted criminals, this article argues that linguistic embodiment was an important dimension of interpersonal communication in the eighteenth-century anglophone world. Advertisements from Britain, North America, the Caribbean, and South Asia are analyzed in order to exemplify perceptions of the linguistic embodiments of wanted criminals. In its zeal to accurately capture personal details that would disclose the being of a person in the world through text, fugitive advertising is a writing practice closely related to techniques of descriptive characterization that begin to appear in anglophone literature around the same time. This article views fugitive advertising as a salient intertext for certain characterological and narrative strategies of the late eighteenth-century novel. William Godwin’s Things as They Are; Or, The Adventures of Caleb Williams (1794) is read as an example of a novel that requires readers to envision characters’ linguistic embodiments and interactions in order to situate them within evolving systems of interpersonal categorization.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0100.028
Scholarly communication0.0090.007
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.231
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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
Published2015
Admission routes1
Has abstractyes

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