Bibliographic record
Abstract
Achieving food and nutrition security is a complex challenge. This is especially true in South Asia, where 40 percent of the world’s poor—who survive on less than US$1.25 a day—live and 21 percent of the population is undernourished. Yet countries in South Asia have seen marked improvements in socioeconomic development in recent years. South Asia encompasses only 3 percent of the world’s land, but is home to about a quarter of the world’s population (1.6 billion people). While agriculture is a critical component of food and nutrition security, it is interlinked with water, energy, infrastructure, and policy challenges. Apart from this, natural resources are under additional pressure due to population growth, income growth, urbanization, changing consumer preferences, and climate change. Against this backdrop, the IFPRI South Asia Regional Office (SAR) in New Delhi engages in evidence-based policy research and capacity-building activities related to food and nutrition security in the region. The research focuses on agricultural diversification, climate change, markets and trade, nutrition and health, science and technology, and governance, contributing directly to the strategic research areas established by IFPRI’s 2018–2020 Strategy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.262 | 0.257 |
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".