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Targeting vulnerability after the 2005 earthquake: Pakistan's Livelihood Support Cash Grants programme

2009· article· en· W2004065337 on OpenAlexfundno aff
Sarah Zaidi, Ahsan Kamal, Naila Baig‐Ansari

Bibliographic record

VenueDisasters · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
FundersLahore University of Management SciencesMcGill UniversityWorld Bank Group
KeywordsLivelihoodCashCash transfersSocioeconomicsVulnerability (computing)GeographyBusinessMedicineFinanceEconomicsComputer security

Abstract

fetched live from OpenAlex

The 7.6 magnitude (Richter scale) earthquake that struck northern Pakistan on 8 October 2005 was devastating. This paper gauges success in targeting vulnerable families during the transition from relief to reconstruction through cash assistance provided by the Livelihood Support Cash Grants (LSCG) programme. Families without a male member, with a disabled male member aged between 18 and 60 years or with more than five children, defined as vulnerable, were provided with USD 50 per month for six months via a bank transfer. The LSCG scheme enrolled around 750,000 families and selected 267,402 vulnerable families to whom it disbursed a total of USD 86.95 million. Using a community-based survey, this paper assesses leakage and under-coverage (exclusion). Approximately 30 per cent of families received the cash grant. However, only one in two was eligible for the benefit, and one in two deserving families was excluded. This is a matter of grave concern.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations16
Published2009
Admission routes1
Has abstractyes

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