Post Disaster Reconstruction after 2015 Gorkha Earthquake: Challenges and Influencing Factors
Bibliographic record
Abstract
The Gorkha earthquake on April 25, 2015 has significantly affected the livelihood of people and overall economy in Nepal, causing severe damage and destruction in central Nepal including nation's capital. 800 thousand buildings were affected leaving 8 million people homeless. Challenge of reconstruction of optimum 800 thousand houses is arduous for Nepal Government in background of its turmoil political scenario and weak governance apart from its difficult geographical terrain. Albeit, with significant number of stakeholders involved in the reconstruction process, no appreciable progress has seen to the ground till date, which is reflected over the frustration of affected people. In order to identify factors hindering timely and quality reconstruction, this research has brought basic arguments and ideas prospected by different actors involved in the process. Methodology of the study is comprised with semi structured interviews with social mobilizers, engineers working in the field, and affected people, group discussion, field observations and regular follow-up of the incidents through national newspapers and discussion forums. This study concludes that inaccessibility, absence of local government, weak governance, weak infrastructures, lack of preparedness, knowledge gap and manpower shortage etc. are the key challenges of the reconstruction after 2015 earthquake in Nepal. Good governance, integrated information, addressing technical issues, public participation along with short term and long term strategies to tackle with technical issues are highlighted as some imperative factors for timely and quality reconstruction in context of Nepal.Journal of the Institute of Engineering, 2018, 14(1): 52-63
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".