Themes Related to Islandness in Tourism Logos: Island versus Non-Island Tourism Destinations
Bibliographic record
Abstract
Islands hold a special place in the hearts and minds of travelers. The depiction of islands as a paradise and the sense of idyllic fantasy that travellers invoke with respect to islands is, in essence, a rudimentary attempt to brand islands. Islands are celebrated as being distinct from non-islands in ways rooted in the place, and the pursuit to discover characteristics that distinguish islands from their non-island counterparts is the quest to understand islandness. It should be no surprise then if some island destination management organizations, responsible for the creation of engaging and compelling brand identities, integrate themes related to islandness in the brands they develop to promote island destinations. This paper examines the incorporation of islandness themes as part of the brands developed to promote tourism by comparing islands and non-islands destinations. The tourism logos used by 85 island- and 146 non-island destinations were reviewed to assess the degree to which the logos included themes related to islandness. Employing a modified Likert-scale, study findings show island themes are not used exclusively by islands, but instead are used to various degrees and in different ways by islands and non-islands alike. This suggests that many of the themes related to islandness are not unique to islands and apply in some cases to non-island destinations as well. In addition, the findings may be interpreted to mean that the investigation of logos as a proxy for understanding islandness in island tourism brand identities is insufficient and inadequate, and a more fulsome investigation into the various ways of expressing brand identity might provide greater insights.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".