The Extent, Nature and Distribution of Child Poverty in India
Bibliographic record
Abstract
Despite a long history, research on poverty has only relatively recently examined the issue of child poverty as a distinct topic of concern. This article examines how child poverty and well-being are now conceptualized, defined and measured, and presents a portrait of child poverty in India by social and cultural groups, and by geographic area. In December 2006, the UN General Assembly adopted a definition of child poverty which noted that children living in poverty were deprived of (among other things) nutrition, water and sanitation facilities, access to basic health care services, shelter and education. The definition noted that while poverty hurts every human being ‘it is most threatening and harmful to children, leaving them unable to enjoy their rights, to reach their full potential and to participate as full members of the society’. Researchers have developed age-specific and gender-sensitive indicators of deprivation which conform to the UN definition of child poverty and which can be used to examine the extent and nature of child poverty in low and middle-income countries. These new methods have ‘transformed the way UNICEF and many of its partners both understood and measured the poverty suffered by children’ (UNICEF, 2009). This article uses these methods and presents results of child poverty in India based on nationally representative household survey data for India.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".