RURAL TOURISM 3.0: CONCEPTUALISING AN INNOVATIVE APPROACH IN MONITORING THE ‘HEALTH’ OF RURAL DESTINATIONS IN MALAYSIA
Bibliographic record
Abstract
With the evolution from Web 1.0 to Web 3.0, web servers are able to dynamically generate rich web information to internet users. The capabilities of Web 3.0 can be used in the tourism sector to manage the industry more effectively. One of the major forms of tourism that is gaining its momentum in Malaysia and requires efficient management is rural tourism. Nonetheless, in the last decade, the concept of rural tourism has melded with mainstream tourism and resulting in it losing its distinctness. Consequently, the tourism industry's growth throughout the years has created an increasing amount of stress economically, socially and environmentally. Hence, the development of a sustainable and responsible rural tourism is needed in fulfilling the objectives of all stakeholders in the system. Thus, the main aim of this paper is to conceptualise a framework to monitor the ‘health’ of rural tourism destinations in Malaysia using Web 3.0 technologies. A rural tourism prototype called the “Rural Tourism 3.0” is developed to assess, advice and monitor the economic, socio-cultural and environmental responsible impact of rural tourism destinations using an integrated real-time decision support system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".