Urban Flood Risk Management: A Study of Adaptation Based on Knowledge of Ethnic Communities on the Banks of the Musi River in Palembang
Bibliographic record
Abstract
Areas lowland topographic in Palembang City have a high risk of flood vulnerability so they need to be able to occupy the area.The study aims to provide insight into the perception of knowledge of multi-ethnic communities regarding adaptive behavior in dealing with flood risks.There are five ethnic communities that have the highest risk of flooding events selected based on the location of settlements located on the riverbank.The research sample was selected based on a population consisting of 119 ethnic households living in houses on the riverbank.The flood knowledge perception index and flood risk response behavior were constructed based on relevant research questionnaire indicators and classified into high and low value scales.The analysis technique for each indicator uses regression to find the influence between respondent knowledge and flood risk adaptation behavior.The results of the study show that Arab ethnic knowledge has a significant influence on anticipatory behavior before a flood occurs.There is a significant influence of knowledge of flood height on the behavior of moving goods to higher places when flooding occurs among the Malay and Chinese ethnic groups.Research conclusion multi-ethnic knowledge of flood areas has a significant influence on adaptive attitudes during floods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".