A importância das marcas na competitividade dos destinos turísticos
Bibliographic record
Abstract
With the arrival of globalization we could realize the strengthen of touristic market because by making the touristic destinations very similar, it also stimulated the competition between those places, and due to this competitiveness, the touristic destinations are investing more in promotion and advertising resources. By realizing this trending, we have developed a work that aimed to make a theoretical review about the communication in Tourism, the marketing, the place marketing, the trademarks and the design, developing a data compile about touristic destinations brands by using official written and electronic documents. Regarding these information, it was built a comparison between four international brands, Portugal, Spain, New York and Toronto, and the brand developed to Rio de Janeiro, in order to appraise what is important to built marks, understanding how it’s concept it is applied to touristic destinations. It was developed a methodology based on a comparative method and this work research is classified as basic and qualitative. Considering the results, it was possible to define its similarities and distinctions, analyzing how the concept of marks is used for each one of the examples starting with the analysis of four base-categories of study.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".