Introduction to special issue on island tourism resilience
Bibliographic record
Abstract
The purpose of this Special Issue is to frame island tourism research while bringing to the forefront the myriad of challenges facing islands to develop successful tourism destinations. Islands are special geographic features spread all across the globe, and tourism has been an important economic activity for many of these often resource constrained territories. If tourism is a means to economic prosperity, then island destinations need to explore several considerations and build resilient tourism economies that can overcome external shocks. While tourism researchers have noted island tourism research in book and article titles, when addressing the occurrence of tourism in islands, the body of work surrounding tourism in islands requires framing, as a wide array of concepts has been explored including sustainability, resilience, development, economies, impact, destinations, trends, planning and prospects. With such variety, island tourism research has seemed to lack direction or form. Herein, this Special Issue seeks to address this by framing island tourism research around the themes of Lifecycles, System Decline and Resilience. Tourism growth and development occur as a process over a period of time and this flow can be illustrated using tourism arrivals. Ongoing flows of visitors are expected to take a particular course and understanding changes in that course relates to identification of system decline. Finally, building resilience means gaining the capacity to adapt to and successfully manage changes in the dimensions and nature of tourism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.100 | 0.036 |
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".