Indigenous Cultural Tourism in Malaysia
Bibliographic record
Abstract
There are almost 500 million indigenous people in the world, in over 90 countries. Each of the indigenous groups has its own culture, belief, and skills. This has made them a very special community in the world. The uniqueness of the indigenous people has attracted people to come to learn and experience their culture. Thus, indigenous culture has been used as part of tourist attractions in many countries such as Australia, New Zealand, and Canada. The tourism sector will benefit the indigenous community and the government itself. The indigenous community gets to improve their socio-economy and will continue to practice their culture and the government gets to improve their revenue through tourism. Malaysia is home to almost 200,000 thousand Indigenous people (Orang Asli) from three main ethnic groups. Thus, Malaysia has implemented the indigenous culture of Orang Asli as part of its tourism sector. Hence, this study is conducted to analyze the law and practice of indigenous cultural tourism in the Orang Asli in Peninsular Malaysia. Apart from that, a comparison will be made to Australia to study its law and practice in promoting aboriginal cultural tourism. However, this study finds that challenges remain in the need to balance the protection of Orang Asli’s culture and socio-economic development. Furthermore, the inefficiency of local management remains a challenge in promoting indigenous cultural tourism in Malaysia. Therefore, Malaysia needs to improve on its management to enhance indigenous cultural tourism in Malaysia.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".