Urban Resilience and Poverty in Malaysia
Bibliographic record
Abstract
This article is a systematic review paper that focuses on urban resilience studies in Malaysia.Generally, resilience is not a new concept that has attracted scholars to conduct numerous relevant studies.Unfortunately, the majority of earlier studies did not consider the urban resilience perspectives towards poverty in Malaysia.This article utilizes three journal databases, Scopus, Web of Science, and MyCite for the review of the current research.The Transparent Reporting of Systematic Reviews and Meta-Analyses method known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted in this article.As a result, 10 articles have been systematically studied as a result of the search efforts.Most crucially, the review was able to develop four key topics based on thematic analyses, including economic resilience, social resilience, environmental resilience, and institutional resilience.However, there are still limited numbers of literature available on urban community resilience in Malaysia and also literature on community resilience towards poverty as compared to studies on resilience towards disaster.In short, the finding of this study hopefully can be a good baseline for the related stakeholders and policymakers in formulating action plans and strategies related to urban and poverty planning in the upcoming years.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".