De-Constructing Wonderland: Surfing Tourism in the Mentawai Islands, Indonesia
Bibliographic record
Abstract
The purpose of this paper is to deconstruct surfing tourist space (Wonderland) in the Mentawai Islands, Indonesia, and to show the distribution of wealth generated through foreign tourists accessing local resources is inequitable and unsustainable. The discovery of world-class surf in this region in the early 1990’s spawned the rapid development of a foreign-controlled surfing tourism industry. This paper seeks to establish the notion of ‘tourist space’ as a conceptual tool for analysing the rise of surfing tourism in Indonesia based on 50 years of narrative, surf exploration and idealized media representations of uncrowded surf breaks and high adventure – in short, Wonderland. In the Mentawai context, a marketing synergy between foreign surf-tour operators, the media, and surfwear manufacturers have written local populations, government, and NGOs out of the ‘Wonderland’ equation. This paper analyses the construction of surfing tourist space in Indonesia by unpacking its components to reveal foundations historically based in surfer mythology. We argue that through a comparison with best practice principles of tourism development, a re-evaluation of self and other, and empowerment of community based organizations that a re-conceptualisation of tourist space may allow new, more effective foundations to be laid in pursuit of sustainable tourism development.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".