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Record W2994726843 · doi:10.9734/ejnfs/2015/20839

Inclusive Social Enterprise to Alleviate Iron Deficiency in Women and Children in Cambodia

2015· article· en· W2994726843 on OpenAlexaff
Gavin R Armstrong, Alastair J. S. Summerlee

Bibliographic record

VenueEuropean Journal of Nutrition & Food Safety · 2015
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIron deficiencySocial enterpriseBusinessEconomic growthPolitical scienceMedicineEconomicsPublic relationsPsychiatryAnemia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.252
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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