AN ANALYSIS OF THE SOCIO-ECONOMIC ANTECEDENTS OF HOUSING INSECURITY
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".