A bibliometric analysis of Indonesian ethnic food studies
Bibliographic record
Abstract
This study examines the evolution and growth of research on ethnic cuisine in Indonesia over a thirty-year period (1993–2023) using a bibliometric analysis. The findings revealed that research in this field has grown with an average annual expansion of 6.7%. Thematic evolution highlights a shift in focus from the cultural and regional aspects of ethnic cuisine to scientific and nutritional dimensions, such as health benefits, functional foods, and modernized practices. Additionally, this study identifies key trends, including the role of gender and age in shaping food preferences, the contrasts between rural and urban dietary behaviors, and influence of globalization on traditional food systems. A trend of collaborative research was observed, with international partnerships involving nations such as Malaysia, Japan, China, and Canada underscoring the global appeal and relevance of Indonesian ethnic food research. This study highlights the growing interdisciplinary and international nature of research in this field. Recommendations for future studies include exploring diverse perspectives, and expanding cross-national research to address the dynamic challenges and opportunities in ethnic cuisine studies.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.134 | 0.229 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".