A Semiotic Model of Destination Representations Applied to Cultural and Heritage Tourism Marketing
Bibliographic record
Abstract
The article argues that semiotic analysis can be applied advantageously in tourism studies. C. S. Peirce’s representation triad is applied to destination representations by conceptualizing destinations, related activities, or entities as objects; photographs or textual descriptions as signs; and potential tourists’ comprehension of the sign as interpretants. Three formal analyses of selected photographs used by convention and visitor bureaus (VISIT FLORIDA, Destination Halifax and VisitDenmark) illustrate how the sign–object relationship is always characterized by a combination of iconic, indexical, and symbolic qualities, each of which destination marketers should consider in choosing representations because of the influence those qualities exert on reception. It is argued that the semiotic model can help marketers make informed decisions about the relevance and probable impact of the iconicity, indexicality, or symbolism of a representation, and that the semiotic model helps avoid conceiving of representations as if they were static rather than dynamic components in an ongoing process.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".