Bibliographic record
Abstract
The research talks about cross-cultural product design which combines cultural tradition and user's behavior and perception.It provides an example of a tea product design, demonstrating the process of how to include Chinese's tea culture and perception about tea drinking from people who live in North America in a cross-cultural product.To understand what are Chinese cultural elements and the users' perceptions about tea, this study first investigated current Chinese cultural products and conducted 100 interviews about drinking tea.In conclusion, Chinese cultural elements mainly used in current cultural products are pattern, form, material, function, historical or literal figures in cute mascot, story/anecdote, and metaphor.The participants mostly regarded drinking tea as a relaxation, social activity, and keeping health.Some of them also related tea with ceremonial, cultural tradition, emotional attachment and atmosphere.In addition, convenience of making tea is a consideration as well.The final tea set design considered the Chinese cultural elements including pattern, form and material and the users' conceptions of relaxation, social relationship, cultural tradition, ceremonial and convenience.This study suggested a method of designing a cross-cultural product, which consists of two main parts.One is to investigate the traditional design elements of one's culture and the other is to learn the users' perceptions.The final design will take both considerations into account.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".