Integrating Blockchain into Smart Tourism: Towards an Enhanced User Experience
Bibliographic record
Abstract
The fast-paced changes of technology have evolved the tourism field and now more than ever it is a well-connected space that orbits around the user— giving birth to what is known as smart tourism. This work investigates the possibilities of fusing blockchain technology with smart tourism frameworks: an approach aimed at boosting the overall user experience. With blockchain offering transparency, security, and decentralization as its core features, stakeholders can enhance their services in addition to winning consumer trust toward effective operations. A clear picture of challenges surrounding adoption is painted: technological barriers and regulatory issues are outlined among them. Through a detailed analysis on how different actors within the tourism sector can interact while using these models, this study proposes a guide that could be used as a roadmap toward implementing blockchain initiatives for smart tourism. The synthesis underscores that marrying blockchain with smart tourism does not only pledge improved operational efficiencies but also promises to take out unessential costs while injecting some level of personalization and most importantly security during user travel experiences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".