Homestay drives community's socio-economic development and sustainability; a case study of Skardu, Gilgit-Baltistan
Bibliographic record
Abstract
Northern Pakistan is one of the most visiting places by both domestic and foreign visitors. This visitation phenomenon encourages residents to commercialize their residences portion a partial sharing into homestays. The northern area tourists have the privilege to experience homestay operations due to a shortage of lodging demand-supply gaps. A homestay is an idea of accommodation operation in which a visitor pays a charge to stay with a host family and engage with the native population. GB in general and Skardu in particular, benefited from the homestay due to national and international tourist flow. Homestay in Pakistan is not standardized or even not regularized. However, there are still many newcomers in the homestay business while owners do not understand business requirements, and the majority of them are not get training. Social media and online booking is popular channels for selling products. This study includes an assessment of community perceptions of how much the Homestay program influences the community in Skardu city, and even a discussion about the issues that Homestay operators and the community face. A cross-sectional research design with both quantitative and qualitative statistics (QUAN-QUAL Techniques) was used to examine the data. The data on a semi-structured questionnaire was collected from 30 homestay operators in the city. This research highlighted numerous characteristics of homestay growth, issues, and prospects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".