Guidelines for the design of a digital marketing strategy of the Cuban Ministry of Tourism aimed at the Canadian market
Bibliographic record
Abstract
Cuban Ministry of Tourism is in charge of managing communication around the Cuba destination. Given the lack of a structure in charge of Digital Marketing in this Organization, this area is neglected, leaving the entities of the Tourism System deprived of a guide for their actions. That is why the author proposes the design of a Digital Marketing Strategy for MINTUR, aimed at the Canadian public and supported by the official CubaTravel tourism portal, as the main way to market the destination. With this purpose, a descriptive, applied and production study is drawn up, under a qualitative approach. The methods and techniques used include documentary bibliographic review, semi-structured interviews, qualitative and quantitative content analysis of both the Ministry and its counterparts, as well as a focus group. All of this allows you to know the aspects of your remote environment, as well as features of the institution itself, its clients and competitors. The main challenge lies in the identification of accurate guidelines to position the ministry as the main reference for the promotion and information of the Cuba destination in the Canadian market. She addresses the topic with a view to the digital transformation of tourism, which is more than a necessity for research, it constitutes it for the nation.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".