Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this chapter, interconnected development of smart universities and smart cities is scrutinized with the focus on highlighting their jointly constructive role in promoting sustainability, technological innovation, and urban resilience. Through bibliometric analysis of 1,058 academic publications spanning 1997–2024, we identify trends, collaborative networks, and emergent themes catalysing these shifts. Using data from the Web of Science database, we applied VOSviewer to analyse co-authorship, keyword co-occurrence, and research clusters. The chapter found that smart universities act as distributed innovation hubs through the integration of IoT, AI, and renewable energy systems to improve learning spaces and optimize campus operations. They echo the qualities of smart cities as they respond to urban issues such as resource management and participatory governance. Such integrating themes are shown in a few case studies on AI-powered energy networks in Arizona State University and eco-friendly campuses in Vancouver. Smart universities will enable the advancement of smart cities by supporting interdisciplinary smart education and a digitally transformed urban ecosystem while also using data governance to promote resilient and inclusive ecosystems through adaptive reuse strategies and stakeholder collaboration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it