Community Vulnerability to Floods and Landslides in Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We addressed the issue of differential vulnerability to natural disasters at the level of village communities in Nepal. The focus lay on the relative importance of different dimensions of socioeconomic status and in particular, we tried to differentiate between the effects of education and income/wealth, the latter being measured through the existence of permanent housing structures. We studied damage due to floods and landslides in terms of human lives lost, animals lost, and other registered damage to households. The statistical analysis was carried out through several alternative models applied separately to the Terai and the Hill and Mountain Regions, as well as all of Nepal. At all levels and under all models, the results showed consistently significant effects of more education on lowering the number of human and animal deaths as well as the number of households otherwise affected. With respect to the wealth indicator, the picture was less clear and particularly with respect to losses in human lives, the estimated coefficients tended to have the wrong signs. We concluded that the effects of education on reducing disaster vulnerability tended to be more pervasive than those of income/wealth in the case of floods and landslides in Nepal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it