Challenges for reconstruction after <i>M<sub>w</sub></i>7.8 Gorkha earthquake: a study on a devastated area of Nepal
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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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".