Bibliographic record
Abstract
【This research examined the design characteristics of Madeleine Vionnet, a female fashion designer who left an enormous legacy and made a great contribution to the high added-value fashion industry. Her techniques can be dividing in to three types: the bias cutting, the design by geometrical methods, and the classical style of ancient Greek clothing. This research also intended to study the design cases in which Vionnet's drapery images are applied to modern fashion, mainly the haute couture works that have appeared at Dior collections since 2000. In terms of the characteristics of Madeleine Vionnet's design, First, she produced the best achievement in dress and ornament history by developing a new technique called bias cutting. Second, her work was groundbreaking because it changed the previously planar approach to the female body into a solid conception by cutting and connecting geometrical pieces in the form of quadrangle, triangle, and a quarter-circle. As a result, her works depicted feminine beauty to the fullest extent through the combination of the human body, excellent materials, and the most sophisticated technology and personal skill. Third, her approach was a classical style tinged with the Greek costume image. With this style, which was born by reinterpreting the key tone of the Greek epoch in a modern way, and transcending and even changing tradition, she created a form of beauty that only she could.】
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".