Bibliographic record
Abstract
This paper examines the evolution of global nutrition policy through historical, scientific, economic, and political lenses. It begins by tracing how nutrition science developed—from early concerns with calorie sufficiency and nutrient deficiencies to later emphasis on protein and micronutrients. Over time, rising rates of obesity and non-communicable diseases led to broader, systems-based thinking about food, health, and equity. The economics section explores how market forces—including food prices, subsidies, trade liberalization, and corporate power—influence dietary patterns, especially among low-income populations. It also considers the role of income inequality and consumer behavior in shaping nutrition outcomes. This paper explores the political economy of nutrition, examining how power, institutions, and economic interests shape nutrition outcomes and policy implementation. While nutrition is often framed as a technical issue, its governance is deeply political. The paper highlights how fragmented institutional structures, donor-driven priorities, and short-term political incentives often undermine long-term, coordinated responses to malnutrition. Drawing on historical analysis and contemporary policy debates, the study advocates for a systems-based approach to nutrition that dismantles siloed thinking, addresses structural inequities, and centers sustainability and equity. By framing nutrition as a public good integral to sustainable development, this paper calls for reimagined governance models, better policy coherence, independent monitoring, consumer empowerment and enhanced accountability in global nutrition efforts.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".