Gaia-X Tourism Ecosystem's White Paper on Data Governance for Tourism Governance
Bibliographic record
Abstract
This paper is a joint publication of the Gaia-X Tourism Ecosystem and the Gaia-X Association (AISBL).It reflects the collaborative work of stakeholders committed to building a trusted, interoperable, and sovereignEuropean Tourism Data Space. In today’s rapidly evolving world, the European tourism industry stands at a crossroads. With informationbeing pivotal in driving decisions and strategies, based on the access of (high quality) data, the necessityfor a robust data governance framework has reached an inflection point. As travel dynamics and consumerbehaviours change swiftly, the tourism sector must adapt its governance to benefit everyone involved—from local businesses to international companies, including the public sector.This white paper explores how effective data management can strengthen tourism across Europe. Startingfrom the vision offered by the European Data Strategy and drawing insights from the Gaia-X initiative, itexplores concepts of digital sovereignty and decentralization in the context of the European tourism dataspaces.In the context of this document, Data Spaces concepts are aligned with the definitions of the Data SpacesSupport Centre (DSSC)
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".