Global Circular Economy Practice: Drivers, Barriers and Strategies for Food System in Indonesia
Bibliographic record
Abstract
The urgent need to overhaul food systems towards more regenerative, inclusive, and sustainable approaches has become a critical concern, particularly with the goal of providing healthy food for a global population that will exceed 9 billion by 2050 and an Indonesian population that will reach 300 million.With the current destructive agricultural framework, significant environmental damage is not the only consequence; there is also a decline in social quality within Indonesia's rural and urban communities.This study aims to design a circular economy implementation strategy formulated through SWOT analysis in order to build a sustainable food system in Indonesia.The research methods used are Systematic Literature Review (SLR), Pareto analysis, and SWOT analysis.The results of this SWOT analysis were used as recommendations to develop Indonesia's food system with a circular economy approach, similar to what has been developed at the global level This SWOT strategy shows that by leveraging existing strengths, addressing weaknesses, capitalizing on opportunities, and overcoming threats, Indonesia can develop a more sustainable and resilient food system through a circular economy approach.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".