Bibliographic record
Abstract
This paper examines the professionalism of tourism in Sudurpaschim province of Nepal. Tourism is a multidimensional discipline which covers various tourism-related phenomenon- accommodation, food and beverage services, recreation and entertainment, transportation, and travel services. All these sectors require competent, efficient and skilled human resources to provide better services. In order to gain all these skills for professionalism employees get training, education and code of ethics through different organizations. So, professionalism of tourism is a process of obtaining competencies, skills, qualifications, and experiences for working effectively and appropriately in tourism sector. This paper is based on both primary and secondary data sources. Primary data are collected from semi-structured questionnaire, field observation, and key informants survey. Secondary data are collected from various sources- published and unpublished documents, journals and e-resources. The finding indicates that Sudurpaschim Province is a potential tourism development area. Its pristine natural and cultural diversities provide foundations for tourism development that encompass sites for emerging both religious and secular contexts. However, professionalism of tourism is a prerequisite for success in tourism industry. It supports to enhance knowledge, skills and practice through education and training programs. It requires a strong policy effort for identifying the effective professionalism that needs for increasing entrepreneurs' skills. It can only be achieved by the collective actions of professional organizations, state government and local governments.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".