Transforming MSMEs towards circularity: an attainable challenge with the appropriate technologies and approaches
Bibliographic record
Abstract
Abstract Moving Micro, Small and Medium Enterprises (MSMEs), in developing countries, from linear “take—make—dispose” production patterns to circular models where inputs and natural resources consumption is minimized, and products and waste reuse is promoted, is still a challenge. Environmental transition initiatives with multiple gaps, based on narrow and corrective approaches, isolated measures, and costly and complex technologies suitable for large companies, limit the incursion of MSMEs into the new circularity. It is in this context that the research aims to understand: How to accelerate the transition of MSMEs in developing countries towards circular production models? A critical literature review guided the design, development and analysis of the case study; a small coffee and pig farm located in Colombia, where a circular transition process was undertaken, acted as the unit of analysis. Interviews, theoretical and practical workshops, on-site measurements, systematic observations, and multi-stakeholder dialogs helped to collect and triangulate the empirical data provided by the case. By following systems thinking and sustainable and circular production principles, low-cost, simple and complementary clean technologies were implemented in the farm-system, resulting in multiple benefits at environmental, social and economic levels. This applied research helped to transform a small rural polluting enterprise into a greener and circular business. The study provides theoretical and empirical contributions to the field of research on the transfer of cleaner production and circular economy to MSMEs, expanding our knowledge on the subject. Graphical abstract
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".