Innovation in island ecotourism in different contexts: Yakushima (Japan) and Tahiti and its Islands
Bibliographic record
Abstract
: This article examines whether the future of tourism in island destinations lies in more and continued innovation on the part of all stakeholders so tourists will consider the extra expense of travelling a worthwhile investment. Islands have long been icons as tourism destinations. However, established destination images can cause a lack in adaptability to changing markets. Though located in different parts of the world and seeking different markets most island destinations would benefit from innovative strategies and products to enhance their attractiveness to high yield visitors. The article uses two examples to analyse the innovative forms that have been adopted in island destinations in the hope they could become models or encourage imitation by other destinations. Two aspects of innovation are discussed. New imaginary can creatively and innovatively (re-)imagine representations; sustainability could be an important innovative pursuit, which requires new narratives for continued tourism growth. These concepts are applied next to the island of Yakushima in Japan and Tahiti and its Islands to determine the main innovative elements used or required to jumpstart the attractiveness of island destinations, though it is recognized that implementation is complex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".