Boosting efficiency and quality in EU public services: The need for a European multi-cloud-first strategy
Bibliographic record
Abstract
Unprecedented Opportunity for Public Service ModernisationEU governments have an unprecedented opportunity to unlock up to EUR 450 billion in annual fiscal savings by modernising public services through advanced multi-cloud solutions, cloudbased AI, and other deep tech innovations.As estimated in this study, this figure highlights the transformative impact of digitised government services -not only in improving service quality and operational efficiency but also in generating substantial fiscal gains that would enhance Europe's economic competitiveness.Multi-cloud adoption is not merely an infrastructure choice but a strategic enabler of modern, agile, and citizen-focused public services.To fully capitalise on cloud adoption, EU governments should embrace a multi-cloud strategy that integrates different public cloud providers and, where necessary, also incorporates sovereign or private cloud environments.Cloud services come in many forms, including Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS).Each model offers unique value propositions such as ease of use, rapid security updates, scalability, and the capacity to integrate novel technological features, such as AI applications, seamlessly.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".