African Indigenous Female Entrepreneurs (IFÉs): A Closed-Looped Social Circular Economy Waste Management Model
Bibliographic record
Abstract
Uncontrolled waste disposal sites remain prevalent in low- and lower-middle-income countries, with organic waste constituting between 50 and 80% of the total openly dumped waste volumes. Waste-to-wealth initiatives focused on biowaste enterprises through female entrepreneurs can advance the eradication of open dumps while creating economic opportunities. This study, therefore, proposes an organizational model that leverages Indigenous female institutions, circular economy concepts, and a closed-loop biowaste management technique that mitigates the open-dump challenge. The Indigenous female entrepreneur (IFÉ) business model leverages circular economy and social circular economy models in the application of a low-tech insect-based biowaste conversion that valorizes municipal solid waste into products that can be reintegrated into the environment and community. The model will be utilized in a Tanzanian pilot study using co-production strategies to derive a sustainable biowaste enterprise. Co-production sees users as authorities in their own circumstances and treats them as primus inter pares with experts, thus facilitating the integration of the relational element of Indigenous societies and motivating cultural appreciation. Conversely, co-production will necessitate revisions to the model in every location where it is applied. The model was successfully test-run in a high-income country, but future research, including the pilot study, will validate the model and highlight innovations.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".