Bibliographic record
Abstract
Despite recent progress shown by some of the indicators of Millennium DevelopmentGoals in Bangladesh, the nutritional status among all children of the country is notso satisfactory. Growing evidence suggest that there exist regional differences in childunder-nutrition in Bangladesh. The present article is an attempt to identify the regionaldifferences of child under-nutrition across six divisions of Bangladesh and to understandsome of the determinants of under-nutrition using DHS-2007 Bangladesh dat. This datafocus on under-nutrition and some of the determinants related to household, child andmother. A multivariate model was employed to study the regional differences of undernutritionstatus among children. Across the divisions, a variation of under-nutrition isobserved among the children. The prevalence of under-nutrition is statistically significantin poor households. Economics status, mothers’ education, children’s age, number of familymembers and duration of breastfeeding are important determinants of under-nutritionacross divisions. Child under-nutrition in Bangladesh is still a concern for the householdwith poor economic status. The article calls for improvement of the economic status of thehouseholds across divisions keeping in view the nature of inequality in childhood undernutritionin the country and its differential characteristics across the divisions.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".