Movement of Domestic Tourist in Malaysia in 2010
Bibliographic record
Abstract
Malaysia is a one of the countries that was awarded with various natural attractions. The variety of race also built colorful culture that always offered variety prospects to tourists. Tourism industry in this country has gained attention as one of the main sectors in generating income. Therefore, many strategies have been planned and implemented to promote Malaysia as one of the main tourist hubs in the world. Tourism involving people movement through time and space, tourist experience in some locations is different and their consumption influenced by the pattern of movement. Pattern and trend of tourist movement influenced by various factors, and it was individualistic whether for domestic tourist or international tourist. Understand how tourist move through time and space is important to be implemented for infrastructure and transportation development, product development, destination and new attraction planning and it also important for management studies impact to social, environment and culture that caused by tourism sector. Using domestic tourist data from 2010 in Malaysia, the objective of this paper is to identify the pattern and trends of domestic tourist movement using GIS applications. In addition, this paper also aims to examine the significance of the destination choices made by the tourists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".