Shared Principles of Ethics for Infant and Young Child Nutrition in the Developing World
Bibliographic record
Abstract
BACKGROUND: The defining event in the area of infant feeding is the aggressive marketing of infant formula in the developing world by transnational companies in the 1970s. This practice shattered the trust of the global health community in the private sector, culminated in a global boycott of Nestle products and has extended to distrust of all commercial efforts to improve infant and young child nutrition. The lack of trust is a key barrier along the critical path to optimal infant and young child nutrition in the developing world. DISCUSSION: To begin to bridge this gap in trust, we developed a set of shared principles based on the following ideals: Integrity; Solidarity; Justice; Equality; Partnership, cooperation, coordination, and communication; Responsible Activity; Sustainability; Transparency; Private enterprise and scale-up; and Fair trading and consumer choice. We hope these principles can serve as a platform on which various parties in the in the infant and young child nutrition arena, can begin a process of authentic trust-building that will ultimately result in coordinated efforts amongst parties. SUMMARY: A set of shared principles of ethics for infant and young child nutrition in the developing world could catalyze the scale-up of low cost, high quality, complementary foods for infants and young children, and eventually contribute to the eradication of infant and child malnutrition in the developing world.
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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 | 0.048 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".