Rethinking destination success: An island perspective
Bibliographic record
Abstract
The global tourism industry has shifted due to COVID-19, with tourismdependent islands facing a dire need to realign and reconstruct their tourism offerings to remain competitive. The traditional mass tourism model that has dominated island development has to be re-examined in this new tourism environment with new mindsets regarding the current conditions for destination success. This paper aims to promote an understanding of destination success in an island context and to identify which determinants are critical during this period to achieve optimal destination success. The findings from this study suggest that island destinations are at a critical turning point, and key strategic shifts are necessary to enable future destination success as defined by the Destination Management Organisations. There is a need to shift from management to stewardship, from product to experience, from quantity to quality, and from stakeholder presence to engagement. Core to these strategic shifts is an incorporation of locals as central to the quality of the overall experience, with less reliance on the natural resources (sun, sea, and sand) to which these island destinations have been beholden to for decades.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".