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Effectiveness of social security net works- Myths and realities

2017· article· en· W2792365153 on OpenAlexaboutno aff
V. Vijaya Lakshmi, Manoj Paul

Bibliographic record

VenueADVANCE RESEARCH JOURNAL OF SOCIAL SCIENCE · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsMythologySocial securityNet (polyhedron)Safety netSociologyPolitical scienceMathematicsLawArtLiterature

Abstract

fetched live from OpenAlex

The social safety net is a collection of services provided by the state or government sector for the welfare of poor people which include welfare programmes, unemployment benefits, elderly people benefits, healthcare, homeless shelters, and sometimes subsidized services such as public transport, which prevent individuals from falling into poverty. The programmes protect the families from the impact of economic shocks, natural disasters, and other unexpected crises; ensuring that children grow up healthy, well-fed, and can stay in school and learn; empowering women and girls; and creating jobs. Social safety nets come in many forms like cash, food, healthcare, housing, household goods or education for children etc. According to the State of Social Safety Nets 2015 statistics, more than 1.9 billion people in 136 low and middle-income countries are now on beneficiary rolls of social safety net programmes. Different countries have different social safety nets to meet the needs of people. Average social expenditure among OECD (Organization for Economic Cooperation and Development) countries was over 21 per cent of GDP in 2014. OECD countries operate different programmes like minimum income programme, housing benefits, family benefits, benefits for lone parents, employment -conditional benefits for able bodied people, child care benefits. The average developing country spends 1.6 per cent of GDP on social safety nets. In Sub-Saharan Africa and South Asia, where most of the global poor live, social safety nets cover just one-tenth and onefifth of the poorest 20 per cent of the population, respectively. The world's five largest social safety net programmes are all in middle-income countries (China, India, South Africa and Ethiopia) and reach over 526 million people.India spends about 0.72 per cent of the Gross National Product (GDP) on social safety net programmes. Pakistan and Bangladesh -spend a higher proportion on social safety net, i.e. 1.89 per cent and 1.09 per cent. In Brazil, under "BolsaFamilia" programme 53 per cent of Brazil's poor (or the bottom quintile) are covered. Mexico established its own conditional cash transfer programme, known as Prospera has been credited with improving education levels, strengthening nutritional status, and reducing poverty. Elements of Prospera have been replicated in more than 50 countries. In cities of China, there are different pension systems for civil servants, public services workers, urban employees and urban residents. Canada provides transfer payments for medicare and public education.The CLMV countries i.e., Cambodia, Laos, Myanmar, and Vietnam have their own framework for food security and social safety nets, as they are heterogeneous

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)0.049
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: none
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.015
Scholarly communication0.0090.013
Open science0.0020.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0210.002

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 head0.033
GPT teacher head0.435
Teacher spread0.402 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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