Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The South Asia Infant Feeding Research Network (SAIFRN) was established in 2007 to foster and coordinate a research partnership among South Asian and international research groups interested in infant and young child feeding. SAIFRN has brought together a mix of researchers and program managers from Bangladesh, India, Nepal, Pakistan, and Sri Lanka together with international partners from Australia. As the first activity, SAIFRN conducted a series of analyses using Demographic and Health Surveys of Bangladesh, Nepal, and Sri Lanka and the National Family Health Survey of India. The results highlight that most indicators of infant and young child feeding in these four countries have not reached the targeted levels. The rates vary considerably by country, and the factors associated with poor feeding practices were not always consistent across countries. Driven by the ultimate goal of improved child survival in the region, SAIFRN wishes to expand its partnerships with governmental and nongovernmental organizations that share common interests both within and outside the South Asia region. In the future, SAIFRN hopes to provide more opportunities to researchers in the region to improve their skills by participating in capacity-building programs in collaboration with international partner institutions, and looks forward to liaising with potential donors to support such activities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it