Analyzing Spatial Distribution of Poverty Incidence in Northern Region of Peninsular Malaysia
Bibliographic record
Abstract
Poverty has been a major problem around the world for many years. Therefore eradicating poverty has become the first agenda in the United Nations Sustainable Development Goals (SDGs) as its achievable target. In Malaysia, poverty rate is relatively low, decreasing from 49.3% in 1970 to 15% in 1990, then, to merely 0.6% in 2014. Although poverty rate is very small, it is reported at a state level which is to general to visualize its actual distribution. Furthermore, it fails to capture geographic variation within the state. This study aimed to analyze the spatial distribution of poverty incidence in the northern region of Peninsular Malaysia at a sub-district level using Geographic Information System (GIS). GIS was used to map poverty rate, demographic burden and poverty hotspot in the study areas. The poverty data were obtained from e-Kasih database. Furthermore, the accessibility of each sub-district to major urban centers, higher education institutions, and health facilities were also measured. Results indicated that poverty rate was highly correlated with regional differentiation, where location played a significant role in identifying areas with a high number of poor populations. Sub-districts with high poverty rate were less accessible to major urban centers, higher education institutions and health facilities. The findings also indicated that access to opportunities and facilities remained the major concerns in solving the poverty issues in Malaysia. It is timely, therefore, a spatial dimensional approach used to complement the existing poverty eradication strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".