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Changing Habits: The Politics and Theatricality of Clothing in Early Modern English Voyages

2024· book-chapter· en· W4414476742 on OpenAlexaboutno aff
Sophie Lemercier-Goddard

Bibliographic record

VenueEdinburgh University Press eBooks · 2024
Typebook-chapter
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsClothingPoliticsKey (lock)Relation (database)PerceptionAdaptability

Abstract

fetched live from OpenAlex

Clothing, and the need to find new markets, is often the economic rationale behind overseas exploration but it also plays a crucial role in the organization and circumstances of the voyages themselves, whether it be the local (and often ill-suited) garb of the travellers, or the foreign and often exotic outfits of the country people they encountered. Sophie Lemercier-Goddard’s chapter turns to the Northern climes and examines European encounters with ‘people of Cathay’ (Frobisher, Davis, Hakluyt) - Inuit and also ‘frozen Muscovites’ (Love's Labours' Lost, V.2). Scenes of travellers and strangers changing habits (European travellers going native or conversely country people, often forcibly, dressed up in a European fashion) changed the early modern visual culture. A closer look at the theatricality of such moments shows that beyond the generic conventions of travel writing, clothing in this case questioned technology and its relation to the elements. It was key in redefining the winter landscape, the perception of distant climes, as well as a general sense of place. Strange clothing testified to the extraordinary tricks weather played on men and women, but also demonstrated the amazing adaptability and versatility of humankind.

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: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score0.671

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 studies0.0000.000
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 head0.032
GPT teacher head0.199
Teacher spread0.167 · 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
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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