Inclusive Social Enterprise to Alleviate Iron Deficiency in Women and Children in Cambodia
Bibliographic record
Abstract
Objectives: Iron deficiency -particularly affects women and children, especially those living in poverty. Lack of iron leads to weakness, hampers cognitive development, reduces the capacity to work and increases susceptibility to disease. Most treatments involve supplementation but these are often not affordable nor available consistently. Such approaches are not sustainable. Methods: We are commercializing a health innovation in Cambodia that, with ensuring social enterprise is included in every step of the process, has the capacity not only to cure iron deficiency but also be a sustainable approach. The innovation is known as the Lucky Iron Fish: a small fish made of iron that weighs approximately 175g. Placing the fish in the cooking pot when preparing meals or sterilizing water releases sufficient concentrations of iron to alleviate iron deficiency (Charles et al., 2011; 2014). Results: In the commercialization process, we are including social enterprise at every step in the process. The fish are made from local scrap metal by local dealers and the quality control and safety assurance are carried out by the Royal University of Phnom Penh -rejected fish are recycled into machine tools. The fish are packaged in containers made of recycled garbage, constructed by landmine victims and the product is sold through local sales teams. Conclusions: The elements of successful social entrepreneurial activities will be discussed.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".