Evaluating the impact of floods on changes in residential choice behavior in Ho Chi Minh City
Bibliographic record
Abstract
The impact of flooding varies among different demographic groups, hence, measuring the effects of flooding on these groups is crucial. The objective is to assess the impact of flooding on various demographic groups, such as poorer/wealthier individuals, those who have/have not lived in flood-prone areas, and differences in their choices of new housing. Utilizing survey data from individual home buyers in Ho Chi Minh City from the third quarter of 2017 to the end of the second quarter of 2018, this study found that overall, flooding significantly negatively affects housing prices. Particularly, the price reduction does not differ significantly among buyer groups, thus attracting those with limited financial resources, as the houses they purchase tend to have lower prices, resulting in a higher proportion of price reduction. This attracts them to higher flood-risk areas. However, for buyers who have previously lived in flood-prone areas, despite financial constraints, they tend to purchase new homes in non-flooded areas. This suggests a trend where residents undervalue the impact of flooding due to a lack of information.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".