Traditional cuisine as a social symbol: A socio-cultural analysis of the collective identity of Indonesian society
Bibliographic record
Abstract
This study aims to explore the role of traditional culinary arts as a symbol of cultural identity and an instrument of Indonesian cultural diplomacy internationally. The research focuses on how regional cuisine reflects local values and community history, and is strategically used in cultural diplomacy through festivals, diaspora restaurants, and national programs such as Spice Up the World. A descriptive qualitative approach is used to analyze representations of traditional culinary arts in mass media, cultural policies, and digitalization practices. Data were obtained from academic literature, case studies (in Australia and Toronto), digital content such as culinary TikTok, and interactive media campaigns. The findings indicate that traditional cuisine not only strengthens ethnic identity and collective memory but also plays a role in shaping international perceptions of Indonesia. Digital media increases visibility but also brings challenges in the form of commodification and the narrowing of cultural meaning. Diaspora restaurants have proven effective as agents of gastrodiplomacy, while government programs enhance national branding through food. The study concludes that traditional cuisine is a strategic medium that bridges local and global values. If utilized holistically and sustainably, culinary can strengthen Indonesia's cultural image while opening up a more inclusive and competitive diplomatic space.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".