A Case Study on the Application of Make-up in Capstone Design - Body Painting Graduation Work Case-oriented -
Bibliographic record
Abstract
As a research on the capstone design works created by the students from a cosmetology college, this study attempted to make body paintings through convergence between capstone design courses and makeup. Specifically, a total of 4 body paintings were created by expressing the culture and tourist attractions of the four countries in a creative and artistic fashion as a graduation project. A total of 24 students from a cosmetology college, who have taken a capstone design course were examined (6 members per group), and four graduation works (body paintings) were created through full-body mannequins. They were designed under the theme of European and American countries, and painting techniques were used with the culture and tourist attractions of the countries as motives. They were also colored, using brushes and aqua colors. Body paintings were created by converging capstone design and makeup. To express them in a creative and artistic manner, four works were created under a different theme: I. France, II. GREECE, III. BRAZIL, IV. CANADA. Students majoring in cosmetology, not experts, were forced to engage in this project to encourage the public to approach body paintings in an easy and friendly way. Furthermore, such body paintings were expressed in a creative and artistic manner in consideration of both industrial and academic needs.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".