What could infant and young child nutrition learn from sweatshops?
Bibliographic record
Abstract
BACKGROUND: Adequate infant and young child nutrition demands high rates of breastfeeding and good access to nutrient rich complementary foods, requiring public sector action to promote breastfeeding and home based complementary feeding, and private sector action to refrain from undermining breastfeeding and to provide affordable, nutrient rich complementary foods. Unfortunately, due to a lack of trust, the public and private sectors, from both the North and the South, do not work well together in achieving optimal infant and young child nutrition. DISCUSSION: As the current debate in infant and young child nutrition is reminiscent of the "sweatshop" debate fifteen years ago, we argue that lessons from the sweatshops debate regarding cooperation between public and private sectors - and specific organizational experiences such as the Ethical Trading Initiative in which companies, trade unions, and civil society organizations work together to enhance implementation of labour standards and address alleged allegations - could serve as a model for improving cooperation and trust between public, civil society and private groups, and ultimately health, in infant and young child nutrition. SUMMARY: Lessons from the sweatshops debate could serve as a model to promote cooperation and trust between public and private groups, such that they learn to work together towards their common goal of improving infant and young child nutrition.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".