Destination Branding of European Russia: An Empirical Investigation of the Web Projected Imagery
Bibliographic record
Abstract
48-4(470) Izvorni znanstveni radPrimljeno: 8. 9. 2016.Web branding is an important strategic tool for building a promising travel experience that is uniquely associated with the destination and that reinforces the emotional connection between the visitor and the place.With the proliferation of the Internet, destination marketing organizations' (DMO) websites became a crucial communication channel for projecting a desirable place brand image.With an attempt to rehabilitate the brand image of Russia, this study explores the following: (1) What are dominant attributes in the brand images of European Russia communicated via the DMO websites?(2) Which words appear most frequently on the DMO websites of European districts of Russia?(3) Is there a discrepancy between the projected brand images and stories it tells in words on the DMO websites?As these perspectives contribute to a better understanding of the induced component of Russia's destination brand image in the online environment, content analysis of DMO websites from three European Russian regions is conducted and practical implications are discussed.The results have important marketing implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".