Income Poverty and Well-Being among Vulnerable Households: A Study in Malaysia
Bibliographic record
Abstract
The paper aims to examine the income poverty status and compare it with the well-being level between different groups among vulnerable households. Vulnerable households for this study were households that consists at least one of the following criteria: income poor, elderly person, single mothers and/or disabled person. Data was taken from the Official Poverty Line Survey conducted in four Malaysian cities representing each region in Peninsular Malaysia. A total of 286 households were conveniently selected. Descriptive statistics such as mean, standard deviation, ANOVA, correlation tests were applied in data analysis. Findings indicated significant differences in household percapita income (HHPCI) among income poverty status groups and significant differences in well-being among different status of income poverty, whereby the non-poor had the highest mean in both (HHPCI & well-being). Also the mean well-being for poor and potential poor groups were much lower than the hardcore poor group. Further results revealed a positive but small relationship between household percapita income and well-being among vulnerable households. Finally, the findings indicated significant differences between income poor status groups and different level in well-being poor groups. It was possible for people to get out of income poverty while remaining in well-being deprivation (ill-being). Findings from this study provide evidences and enhance understanding in income poverty, well-being and correlates of both especially among vulnerable households.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".