Smart infrastructure and digitalization: content analysis of METRO'24 RockLine, Slovakia
Bibliographic record
Abstract
Smart infrastructure has become a key aspect of urban development, driven by digitalization and sustainability goals. The integration of technologies such as artificial intelligence, big data analytics, and IoT is reshaping cities, making them more resilient, efficient, and sustainable. Digital transformation facilitates urban life while addressing environmental and economic challenges. Collaboration among stakeholders in IT, spatial planning, and sustainability is crucial for fostering innovation, ensuring inclusivity, and addressing urban challenges. In Slovakia, Metro'24 RockLine brought together policymakers, industry leaders, researchers, and urban planners to discuss opportunities and challenges in data-driven societies and smart infrastructure. The event served as a platform for knowledge exchange and strategic partnerships, emphasizing the importance of data-driven decision-making in urban development. Discussions focused on integrating emerging technologies, regulatory frameworks, and the socio-economic impacts of smart city initiatives. This qualitative study analyzed the event through content analysis of textual and audio-visual materials presented during the conference. Findings identified five dominant themes: cybersecurity, virtuality and energy ecosystems, artificial intelligence in tourism, intelligent buildings and districts, and electromobility. These topics reflect the increasing role of technology in shaping modern cities. Despite certain limitations, the study provides valuable insights for academia, industry, and the general public. It contributes to the growing body of research on smart cities, offering perspectives on how digital innovations can improve urban environments. Industry professionals can leverage these insights to refine technological applications, while policymakers can align regulations with emerging trends. Future research should explore the long-term impact of smart infrastructure, the scalability of solutions, and the socio-economic effects of digital integration. As cities continue evolving, the synergy of technology, governance, and sustainability will be essential for creating efficient, inclusive, and livable urban environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".