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Record W4393183499 · doi:10.56976/rjsi.v6i1.177

Homestay drives community's socio-economic development and sustainability; a case study of Skardu, Gilgit-Baltistan

2024· article· en· W4393183499 on OpenAlexaff
Mohammad Alam, Muhammad Danish, Ahmad Faraz

Bibliographic record

VenueResearch Journal for Societal Issues · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsTourismBusinessMarketingSustainabilityVisitor patternPopulationEconomic growthGeographyEconomicsEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.119
GPT teacher head0.504
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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