Beyond Ruins: The Role of Cultural Capital in Post-Disaster Tourism Revival in Kathmandu Valley
Bibliographic record
Abstract
This study offers an in-depth analysis of the nexus between cultural capital, tourism, and disaster recovery, with a particular focus on the aftermath of the 2015 Gorkha earthquake in the Kathmandu Valley, Nepal. Utilizing Yin's (2003) case study methodology, the research illuminates the multifaceted role of cultural capital—embracing its embodied, objectified, and institutionalized facets—in driving post-disaster recovery processes within the tourism sector. Results underscore the intricate challenges and benefits faced by tourism post-earthquake, emphasizing the critical interplay between local cultural heritage and economic vitality. The study identifies the indispensable contributions of institutional cultural capital in spearheading reconstruction efforts and fostering community collaboration. Recommendations highlight the importance of international partnerships, diversified tourism strategies, and community engagement for bolstering post-disaster resilience. While the research enriches the current discourse on cultural capital and disaster recovery in Kathmandu Valley, it advocates for expanded investigations encompassing diverse hazards, tourism modalities, and capital forms to guide sustainable post-disaster development strategies. \n \nThis master’s thesis is written in the manuscript style and contains the following sections: Chapter 1 – Introduction; Chapter 2 – Literature Review; Chapter 3 – Research Design and Methodology; Chapter 4: Manuscript titled Beyond Ruins: The Role of Cultural Capital in Post-Disaster Tourism Revival in Kathmandu Valley; Chapter 5 – Conclusions and Future Research Needs; References; and Appendices.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".