Revitalizing Indonesia’s Local Food Through the Slow Food Movement for a Sustainable Future
Bibliographic record
Abstract
Indonesia's food security faces growing challenges from climate change, uneven distribution, and the dominance of industrial food systems that threaten biodiversity and cultural heritage.This study explores the potential of the Slow Food movement to address these issues by promoting sustainability, biodiversity preservation, and the revitalization of local food traditions.Drawing on literature, policy reports, and case studies, the analysis shows how Slow Food, guided by the principles of "good, clean, and fair," offers an alternative framework to tackle ecological, social, and cultural vulnerabilities.The movement emphasizes local production, short supply chains, farmer empowerment, and sustainable consumption practices.In Indonesia, Slow Food has been implemented through community initiatives, educational programs, and culinary tourism, showing potential to improve farmer incomes, expand access to nutritious food, and safeguard culinary diversity.However, adoption is limited by low public awareness, perceptions of elitism, policy bias toward industrial agriculture, and competition from fast and ultra-processed foods.To address these barriers, this study proposes a roadmap for integrating Slow Food principles into national and regional strategies through multistakeholder collaboration involving government, civil society, private actors, and academia.The findings conclude that Slow Food holds significant potential to contribute to a resilient, inclusive, and sustainable food system in Indonesia, provided that structural gaps and policy misalignments are addressed through coordinated action.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".