Enablers and Barriers of Implementing Circular Economy for Micro and Small Manufacturing Enterprises (M-MSEs) in West Sumatera
Bibliographic record
Abstract
This study aims to analyze the barriers and enablers to implementing a circular economy (CE) at M-MSEs in West Sumatera, identify M-MSEs in West Sumatera that can implement a circular economy, and identify the influencing factors.Questionnaire were distributed to 110 respondents from several M-MSEs in West Sumatera, Indonesia, from March to September 2022.Descriptive analysis and Pearson Correlation was employed in data analysis, and result showed that several CE practices have been implemented by M-MSEs in West Sumatra, thus supporting the notion that CE implies a systemic approach to increasing firm value.In particular, resource-efficient production processes have been widely implemented, namely 36%; this achievement is undoubtedly relatively high compared to the rarity of M-MSEs, which use residual materials in the production process.The most significant barrier to implementing CE that employers feel is the lack of financial support in implementing CE.However, companies that have started implementing CE see it as a business enabler rather than a cost, so CE can be an added value and innovation of the products they produce.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".