Changing Habits: The Politics and Theatricality of Clothing in Early Modern English Voyages
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".