Challenges for reconstruction after <i>M<sub>w</sub></i>7.8 Gorkha earthquake: a study on a devastated area of 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
The Gorkha earthquake on April 25, 2015 had significantly affected the livelihood of people and the overall economy in Nepal. The earthquake had caused damage to about half a million private and public buildings, apart from damage to other infrastructures including schools, hospitals, roads, hydropower, irrigation canals, etc. The earthquake had affected the lives of 8 million people. With significant numbers of actors and stakeholders involved in the reconstruction process, no significant relief has reached the ground or is observable even after 3 years of the disaster. The government has formed National Reconstruction Authority (NRA) as the focal authority for the reconstruction process which is leading the reconstruction process with line agencies and other stakeholders. The longitudinal study was carried out through semi-structured interviews with the engineers working under NRA, local people and social mobilizer, group discussions, and field observation from June 2015 to August 2016 focusing on challenges for timely and quality reconstruction. The research also reviews the experiences from past events in similar social and political condition. This study concludes that the situation was the result of larger institutional gaps as the absence of local government, lack of coordination, bureaucratic hurdles and political transition, weak governance and cross-cutting issues as accessibility, manpower shortage, knowledge gap and other socio-cultural aspects. Authors supplement that the good governance and strategic incorporation of social and cultural aspects of reconstructions along with the technical cross-cutting issues like skilled labour, resources availability and construction knowledge could help to expedite the reconstruction process.
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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 | 0.000 | 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 it