14 Poverty Decline in India in the 1990s: A Reality and Not an Artifact
Bibliographic record
Abstract
Abstract This chapter addresses the problems of comparability of the size distributions from the NSS 50th (1993–4) and the 55th (1999–2000) rounds of Consumer Expenditure Surveys (CES) arising from the use of a mixed reference period (MRP) in the latter survey, as opposed to the uniform reference period used in the 50th round, and from the canvassing of consumer expenditure on food items on two recall periods (7 days and 30 days) from the same set of households. These are resolved by an analysis of unit-level records of the 50th round CES to generate comparable estimates on MRP and of the 55th round employment–unemployment survey (EUS) to settle the 7-day/30-day problem. Generating comparable estimates on five measures of poverty, it is shown that in the 1990s poverty declined in both rural India and in the country as a whole on all five measures, while in urban India it declined on all measures of poverty except the number of urban poor. An analysis of the 61st round (2004–5) CES points to a slowdown in the pace of poverty reduction in rural areas and a worsening of poverty in urban areas between 2000 and 2005.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".