Breastfeeding, Wage Labor, and insufficient milk in peri-urban Kathmandu, Nepal
Bibliographic record
Abstract
This article presents a case study of breastfeeding mothers who are working as carpet-makers in peri-urban Kathmandu, Nepal. A sample of women surveyed about their current infant feeding practices revealed that half of the infants aged three to four months had been introduced to non-breast milk foods and liquids. During in-depth interviews some mothers explained that they supplemented breastfeeding with either milk or solids if they felt that they did not have enough breast milk for their infants. Reports of insufficient milk (IM) among these Nepali women is discussed within the larger context of IM as a worldwide phenomenon that is often associated with the cessation of breastfeeding and the switch to bottle-feeding based on commercial milk products. On average, the women in this study breastfed their infants until the latter were approximately three years of age. A status quo method for determining median duration of breastfeeding indicates that there is no significant difference in the duration of breastfeeding between mothers who work in carpet-making factories and those who spin wool at home. It is argued that reports of IM in this setting are not associated with the abandonment of breastfeeding, for a number of reasons including: the cultural approbation of breastfeeding; the low usage of baby bottles among peri-urban mothers, and the flexible labor practices of the carpet-making industry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".