Grouping the Americas and Asia-Pacific Countries based on Their ICT Readiness, Prioritization of Travel & Tourism and Tourist Service Infrastructures
Bibliographic record
Abstract
Travel and tourism competitiveness is a multi-pillars construct, each originating from measurement attempts. Involvement of governments, technology and tourism infrastructures are viewed as factors able to improve tourism competitiveness. This paper identifies principal components from these pillars for the Asia-Pacific and Americas considered most improved regions by the World Economic Forum (WEF). Five principal components were identified but three components were the most appropriate solution. A cluster analysis divided the countries into four groups offering a classification of the set. With their position presented in a scatter plot, countries can identify and select the best recommendations to their case depending on the group they belong to. Based on the cluster analysis results, it is seen that groups likes the Underachievers and Critical improvements would definitely need to improve their ICT level, while the Average Performers could as well benefit from technology improvements, although at a lesser degree. Specific examples discussed give better indications and recommendations to the countries as they can draw example from the explained cases to either adopt or adapt relevant strategies that would help their economy or avoid making similar mistakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".