Dark Tourism: A Preliminary Study of Barpak and Langtang as Seismic Memorial Sites of Nepal
Bibliographic record
Abstract
Dark tourism is about more than a simple fascination with death, it is also a powerful lens that allows contemporary life and death to be witnessed and relationships with broader societies and culture recognized (Stone, 2013; Allman, 2017). Information about disasters and their effects to the human being draws attention to the people whoever interested to death and disaster and play very important role to attract and motivate the visitors to those places. So far, disaster tourism is also popular as dark tourism because historical and cultural identity is devasted and violent death of a large number of people occurs in the seismic memorial sites. Individuals who are participating in disaster tour are very much curious to see the impact of disaster. This article focuses on Dark Tourism: A Preliminary Study of Barpak and Langtang as Seismic Memorial Site of Nepal. Barpak was the epicentre of earthquake 2015 which caused huge suffering in the western and middle part of Nepal. Langtang is also the place which was doubly devastated. The earthquake struck, landslides and avalanches that destroyed the settlements. Through three data sources: document review, interview and direct observation, this article assesses theoretical understanding of the dark tourism, the society and culture of the seismic memorial sites, the motivation of the visitors, changing trend of visitors in Barpak and Langtang over pre, during and post-Earthquake 2015 and prospects and challenges of dark tourism in Barpak and Langtang. The study finds that the motivation and benefit to visit Barpak and Langtang are; black spot, history & heritage, cultural values, heritage & identity, survivors’ guilt, death and dying, disaster and identity, acts of memory, people’s resiliency, empathy, remembrance, education, entertainment and edutainment which are very much important in promoting dark tourism in Barpak and Langtang.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".