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Record W3021637446 · doi:10.1596/1813-9450-9231

Welfare Dynamics in India over a Quarter Century: Poverty, Vulnerability, and Mobility during 1987-2012

2020· book· en· W3021637446 on OpenAlexaboutno aff
Hai‐Anh Dang, Peter Lanjouw

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2020
Typebook
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersUnited Nations University World Institute for Development Economics Research
KeywordsQuarter (Canadian coin)Vulnerability (computing)PovertyWelfareDevelopment economicsDynamics (music)GeographyPolitical scienceEconomic growthEconomicsSociologyComputer securityComputer scienceArchaeology

Abstract

fetched live from OpenAlex

This paper analyzes the Indian National Sample Survey data spanning 1987/88-2011/12 to uncover patterns of transition into and out of different classes of the consumption distribution. At the aggregate level, income growth has accelerated, accompanied by accelerating poverty decline. Underlying these trends is a process of mobility, with 40-60 percent of the population transitioning between consumption classes and increasing mobility over time. Yet, the majority of those who escape poverty remain vulnerable. Most of those who are poor were also poor in the preceding period and, thus, are likely to be chronically poor. The characteristics of upwardly mobile households contrast with those of the poor; these households are also far less likely to experience downward mobility. The paper also finds that states exhibit heterogenous mobility patterns.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.265
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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