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Record W2967869001

AN ANALYSIS OF THE SOCIO-ECONOMIC ANTECEDENTS OF HOUSING INSECURITY

2018· article· en· W2967869001 on OpenAlexaboutno aff
Steven Henry Dunga, Wcj Grobler

Bibliographic record

VenueDergiPark (Istanbul University) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsJob insecurityFood insecuritySocioeconomicsDemographic economicsBusinessGeographyEconomicsWork (physics)EngineeringFood security
DOInot available

Abstract

fetched live from OpenAlex

The study of poverty has remained in the forefront of both development practitioners and researchers alike; however, the number of poor people remains high throughout the world. Housing security, food security and water security can be seen as central to urban poverty alleviation, and form part of a declaration of the Habitat Conference in Vancouver Canada, in 1976. Housing insecurity is consequently one of the so many faces of poverty. This paper analyses the socio-economic antecedents associated with housing insecurity and homelessness. There are a number of definitions of housing insecurity, of which homelessness is the extreme. The impact of housing insecurity is even severe among children and becomes perpetual due to the consequences of homelessness, which include no schooling, poor health and exposure to crime. This paper presents the conceptualisation of housing insecurity and a review of the socio-economic antecedents of housing insecurity and its extreme state of hopelessness. The paper uses the general household survey data collected by STATSSA with a sample of 21 601 households. A regression model is employed in determining the household’s characteristics that are associated with housing insecurity. Income food security status and material of the structure were some of the factors that significantly predicted household housing insecurity. The paper also proposes a framework to develop a succinct measure of housing insecurity as a second step in the series of developing the literature on housing insecurity in South Africa.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.269
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.263
Teacher spread0.244 · 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 teacher head, 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueDergiPark (Istanbul University)Same topicUrban and Rural Development ChallengesFrench-language works237,207