The Body-dress Symbiosis of Eighteenth-century Menswear: Demonstrating the Need for Digital Technology via George Washington Artifacts
Bibliographic record
Abstract
What to do with artifacts and how to draw and share knowledge from them was addressed by Harris in 1977 when she stated how dress artifacts “can be studied and examined by students and scholars for construction and other design details; or they can be placed on exhibit to be viewed and appreciated by the general public” (Costume Display Techniques, 1977). She reminded her reader that safety is paramount but textiles are intrinsically fragile and many garments survive in poor condition. “Exhibitability” can affect their ability to be collected and displayed (Scaturro and Fung, 2016). Others have discussed the use of digital surrogates and how digital technology is “enabling new forms of collecting and conserving […] and dissemination” (Eastop and Brooks, 2016). Using George Washington garments, the research demonstrate the body-dress symbiosis in eighteenth-century menswear and the difficulty of conveying dress practices accurately unless this is done using digital technology.
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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 | 0.001 |
| 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 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".