Bibliographic record
Abstract
In recent decades, the culinary industry has experienced a significant fusion of East Asian and Western culinary cultures, creating innovative dishes that integrate traditional ingredients and cooking techniques from both regions. This cross-cultural exchange is becoming increasingly common in restaurants, homes, and culinary schools around the world, driven by globalization. East Asian cuisine is renowned for its fresh ingredients, balanced flavors, and beautiful displays, attracting Western audiences. The diverse flavors and abundant ingredients of Western cuisine also inspire East Asian chefs. The resulting fusion has led to popular culinary trends. This interaction has also led to new culinary innovations and dining experiences like the integration of food creation. In addition to professional kitchens, this exchange has also influenced home cooking driven by media and food blogs, and has impacted the food industry, with a growing demand for cross-cultural ingredients and products. The integration of East Asian and Western culinary cultures has enriched the global culinary landscape, broadened the tastes of food enthusiasts, and enhanced people's appreciation of diverse culinary traditions, and continued globalization may continue to change these culinary experiences.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".