A Study of Climate Variability and Socioeconomic Impact on Tourism Industry of Nepal
Bibliographic record
Abstract
<p><em>In Nepal, tourism is the second largest industry after agriculture. Furthermore, Nepal offers numerous opportunities for adventurous outdoor recreation, especially in the Himalayas, and attracts tourists from all over the World. However, future prosperity of Nepal’s tourism industry might be affected by a changing climate and a socioeconomic environment. A log-level regression model is employed to estimate the impact of climatic and socioeconomic factors on the number of international tourists visiting Nepal. Climatic estimates indicated that an increase in temperature did not have a significant impact on the numbers of international tourist arrival. Results show that the number of international tourists visiting Nepal was positively associated with GDP and population growth, inflation rate, and an exchange rate. These results help policy makers for facilitating growth of tourism industry and its adaptability to climate change in Nepal. </em></p><p><em><br /></em></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".