Flood adaptation by informal settlers in kathmandu and their fear of eviction
Bibliographic record
Abstract
Informal settlements in Kathmandu are increasing in size and number.The housing demand in the city is rising due to the population growth and rural-urban migration, resulting in rising housing price.The high cost of housing means it is difficult for the low-income group to afford necessary housing.The government has not addressed the necessity of affordable housing for the low-income group.In this situation, some people of low-income build their dwellings on public land without legal title.This phenomenon has added to the number of informal settlers in the city.Most of the informal settlements in Kathmandu are located in the floodplains of rivers, putting them at flood risk.Annual monsoon season flood incidents in recent years demonstrate how these riverbank informal settlements are at risk.Informal settlers need to take initiatives themselves to reduce their flood risk as the government assistance is absent.Moreover, the government considers the informal settlements unlawful as they are built on public land without any authority.Therefore, there is always the possibility of their eviction by the government.The study investigates how informal settlers perceive their fear of eviction and how the fear influences their flood adaptation.The study pursues the qualitative approach to understand and analyse the informal settlers' fear of eviction and their flood adaptation.Semi-structured interviews were conducted in 41 houses from three informal settlements situated along the largest river of Kathmandu called the Bagmati River.The flood adaptive measures implemented in these houses were also identified.The study finds that the informal settlers can be encouraged for the flood adaptation by reducing their fear of eviction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".