Community-Based Flood Risk Management: Empowering Local Responses: A Case Study in Meru, Klang
Bibliographic record
Abstract
With an expansive flood-prone region, effectively managing risks, particularly those impacting residents in flood-prone communities, poses a significant challenge for Malaysia. Regular in-depth studies are essential to enable the country to anticipate and comprehend the emerging risks that capable of causing both loss of life and damage to properties and public infrastructure. This research aimed to investigate the dynamics of flood risk management and present recommendations for stakeholders in mitigating the consequences of flood events. Employing a qualitative approach, 13 participants, all flood victims residing in Meru, Klang, were purposefully selected. The participants were divided into two groups: the first group, comprising 5 participants, engaged in a focus group interview, while the second group, consisting of 8 participants, responded to open-ended questions. Both groups answered identical structured questions in both oral and written formats. Thematic Analysis (TA) was applied to analyse the data from these exercises. The findings revealed that flood risk management comprises four components: psychological risk, improper development risk, evacuation risk, and communal risk. The study recommends the implementation of a comprehensive flood mitigation plan covering pre-, during, and post-flood phases to address the specific requirements arising during flood events. It is hoped that this research contributes valuable insights to augment the existing flood management system, benefiting not only the victims but also all stakeholders involved in managing the impacts of flood events.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".