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Record W4388961696 · doi:10.1101/2023.11.23.23298962

South Asia’s unprotected poor: a systematic review of why social protection programs fail to reach their potential

2023· review· en· W4388961696 on OpenAlexaff
Warda Javed, Zubia Mumtaz

Bibliographic record

VenuemedRxiv · 2023
Typereview
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPovertyDocumentationPolitical scienceSocial protectionAppropriationEconomic growthPublic relationsBusinessDevelopment economicsPublic economicsEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract The incongruity between South Asia’s economic growth and extreme poverty has led to a growing interest in social protection and the subsequent implementation of anti-poverty programs. These work to promote inclusive growth and ensure that the poor do not get left behind. However, many programs have systematically failed to achieve their full potential in reaching the poorest of the poor. We reviewed the literature to understand the determinants behind this inequity in South Asia. A search of four databases, EconLit, Global Health Database, MEDLINE, SocINDEX, supplemented by citation tracking and an external search, yielded 42 papers evaluating 23 social protection programs. All articles were assessed for quality using the GRADE and GRADE CERQual criteria. Data were analyzed using Thomas & Harding’s thematic synthesis approach to generate new higher-order interpretations. Our analysis identified five themes underscoring program processes that stop resources from reaching the poor. These include: (1) structurally flawed program theories that overlook the complexities of poverty and are instead rooted in simplistic cause-and-effect approaches overestimating the poor’s gain from participation; (2) elite capture of program resources through the direct appropriation of benefits, their powerful positioning in program implementation, and their ability to dictate the poor’s accessibility through relationships of patronage and withholding of information; (3) insufficient targeting strategies to reach the poorest and a subsequent redirection of resources toward the rich; (4) program designs that overlook gender-based restrictions, hidden costs, the poor’s lack of legal documentation, and their physical and social exclusion; (5) some of the poorest households actively choosing self-exclusion from social protection due to a desire to maintain dignity, a lack of capital, and a perception of programs as substandard. The review highlights the disconnect between social protection program designs and the ground realities of their ‘ideal’ beneficiaries: the poorest of the poor. We propose the persistence of this well-documented disconnect may stem from three sources. First, there is an unclear understanding of who the poor are in South Asia, with definitions overlooking the historical influence of the caste system. Western perceptions of poverty continue to dominate the discourse. The second challenge is effective engagement and co-production of knowledge with the poor. Lastly, despite encouragement of international collaboration, fast-paced funding calls do not allocate sufficient time to build relationships with the poor primary stakeholders. We suggest the possibility that maintenance of this disconnect is intentional, reflecting a broader power dynamic in which the global and local elite dictate the lives of the poor based on geopolitical interests and national priorities.

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)0.028
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0210.022
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.329
Teacher spread0.263 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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