Tourism Developments Increase Tsunami Disaster Risk in Pangandaran, West Java, Indonesia
Bibliographic record
Abstract
Abstract On 17 July 2006, the tourist resort of Pangandaran on Java’s south coast was hit by a tsunami, resulting in 413 fatalities and severe damage to buildings. The tsunami resulted in major rebuilds with a focus on mass tourism. Assessments of the impact of a future tsunami focussed on building development and suggest limited change since 2006. This article presents a case study on the development of (largely domestic) tourism in Pangandaran and how this has increased the tsunami disaster risk. Tourist numbers were stable at about 900,000 visitors a year prior to the tsunami, down to slightly over 250,000 visitors a year in its aftermath, and from 2007 onwards numbers are doubling every three years to about 4 million visitors in 2019. The increase has been most pronounced during weekends. Prior to 2006, Pangandaran was characterized by wooden structures and one- and two-story buildings of clay-brick masonry; by 2019, 14 three to six-story hotels have been erected along the waterfront. With many more visitors, most of whom are unfamiliar with tsunami risks, and shelter facilities for less than a quarter of visitors during peak times, future impacts and the potential cost to life are considerably higher now than in 2006, especially if a tsunami were to hit over a weekend. All tourists upon arrival and throughout their stay should be better informed about the risks of tsunamis, and of the location of tsunami shelters and evacuation routes.
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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 | 0.001 | 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) | 0.003 | 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".