The co-production of disasters: how the nexus of climate change, tourism, and COVID-19 increases socioeconomic vulnerability in Mustang, Nepal
Bibliographic record
Abstract
How do high mountain communities, facing the grave effects from climate change and economic impacts of the COVID-19 pandemic on the local tourism industry, perceive and navigate multiple protracted disasters? This article takes up this question from the perspective of a specific mountain community, that of Mustang, a culturally Tibetan region of Nepal bordering the Tibet Autonomous Region (TAR), China. Our findings stem from collective ethnographic research conducted with Mustangi communities in Nepal and among the diaspora in New York City to investigate the nexus between high mountain livelihoods, particularly tourism, and the consequences of two distinct yet interlocking disasters: climate change and the global health crisis of the COVID-19 pandemic. We argue that the pandemic has undermined elements of Mustang’s economic future and simultaneously prompted a resurgent appreciation for and reliance on more traditional modes of community governance and social support. The fact that these dynamics are unfolding amidst ever-present concerns over the effects of climate change in the Himalayas, against the backdrop of labor- and education-driven outmigration, adds a profound layer of complexity to thinking about the future of tourism but also of Himalayan lives, from built infrastructures to the community resilience needed to sustain both.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".