The political economy of 2015 Nepal earthquake: some critical reflections
Bibliographic record
Abstract
A massive earthquake of 7.6 magnitudes on 25 April 2015 and a major aftershock of 6.8 magnitudes on 12 May 2015 hit central Nepal. The earthquake took the lives of about 9000 people, injured about 24,000 and affected one-third of Nepal’s total population (28 million). Despite a huge amount of money (US$ 4.4 billion) pledged by the international community, reconstruction works could not take place on time. Using participatory approach to reconstruction and development as a theoretical framework and reflexivity as a methodological tool, this paper argues that the delay in reconstruction was caused by the inability of the Government of Nepal (GON) as well as the international community, mainly donors, to encourage local participation. The amount of loan pledged by the international community has increased Nepal’s debt stock rather than really helping those who are affected by the disaster. The paper concludes that the modernist top-down model of development – that both government and donors take for granted – has created roadblocks towards understanding Nepal’s contextual realities. Sustainable reconstruction and development cannot be achieved without strengthening the capability of local communities.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".