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Record W4251743269 · doi:10.3138/ecf.23.2.347

Clothes without Bodies: Objects, Humans, and the Marketplace in Eighteenth-Century It-Narratives and Trade Cards

2010· article· en· W4251743269 on OpenAlexvenueno aff
Chloe Wigston Smith

Bibliographic record

VenueEighteenth-Century Fiction · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeAgency (philosophy)ClothingObject (grammar)AestheticsSociologySubject (documents)Nexus (standard)CommodityHistoryLiteratureLawArtPolitical scienceBusinessComputer scienceSocial scienceWorld Wide WebPhilosophyLinguistics

Abstract

fetched live from OpenAlex

While recent studies of the things of literature call attention to the narrative and psychological slippage between people and their possessions, this essay argues that rather than representing a loss for human agency, humans and things intermingle to the disadvantage of objects. I show how trade cards and object narratives engage with the same nexus of commercial culture, objects, and humans, and share a mutual resistance to “autonomous garments”—petticoats, shoes, gowns, and other garments depicted independently of the human form. Object narratives, read in tandem with trade cards, suggest that the growth of distance between persons and things, as opposed to their collapse into each other, constitutes a central narrative in the period's commodity culture and fiction. Object narratives, even as they transform coats, waistcoats, petticoats, slippers, and shoes into first-person narrators, actively work against the entanglement of human and material spheres. Together these genres place sartorial commodities under human control, emphasizing the human subject's agency over those items worn closest to the self.

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)0.002
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 score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.028
Scholarly communication0.0090.008
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.222
Teacher spread0.209 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

Explore more

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