Poverty in Multidimensional Perspective: Policy Insights from Selected North Indian Districts
Bibliographic record
Abstract
This article examined the multidimensional aspects of poverty in selected North Indian districts using the Alkire–Foster counting method of estimating poverty incidence and intensity. Whereas more than half of the sample households are found to be vulnerable to poverty, about a quarter of households are observed to be in the grip of poverty in these districts. Among the dimensions of deprivations, education, standard of living and economic and social security are critical in contributing to multidimensional poverty and vulnerability. In these dimensions, people are mostly deprived of fuel for cooking, sanitation, ownership assets, informal jobs and social security measures. Therefore, the policymakers ought to be proactive in understanding the socio-economic structure of these districts to formulate inclusive distributive policies as appropriate area wise. However, policies such as urbanization, promotion of technical/vocational education, initiation of micro and small entrepreneurial activities completing and supplementing to farm activities and introducing measures of social protection can help people come out of the tragedies of poverty.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".