Nutrition through the Lens of Human Development-Capability Approach: Conceptual Framework and Its Application
Bibliographic record
Abstract
This paper presents an integrated conceptual framework for analyzing nutrition from a human development-capability perspective. The framework, drawing on Amartya Sen’s entitlement theory and capability approach, integrates the elements of entitlements, endowments, conversion factors, agency, and the distinction between primary and enhanced capabilities. It provides a comprehensive lens through which nutrition can be understood and addressed, offering a more holistic view than the traditional nutrient-deficiency-focused approach. The framework's unique focus on generating individual capabilities and freedoms, rather than resource availability, promises to enhance our understanding of how resources are converted into nutritional outcomes. The study provides a comprehensive multisectoral analysis of malnutrition by examining personal, cultural, social and economic characteristics, as well as public policies within the human development-capability approach. The framework proposed in this paper plays a crucial role in understanding the intrinsic value of nutrition. It offers recommendations for addressing nutrition by enhancing entitlements, improving conversion factors, and achieving nutritional security, thereby contributing to broader human development outcomes. The study offers policy tools related to gender empowerment, nutrition education and awareness, water and sanitation among many others to ensure enhanced capabilities for achieving higher levels of nutrition security.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".