To integrate or not to integrate? A matter of choice for universities
Bibliographic record
Abstract
What is the right location for a university campus? Universities have a preponderant role in today’s societal models. They have been in the core of development — economic, social, sustainable, inter alia — and their role within urban context has changed in order to respond to the university mission — that nowadays includes of civic engagement as well as a stronger participation in economies, through the development of startups and innovation ecosystems. This paper relies on the premise that, even in a post-pandemic world, the Campus is still a window to the world, it can shape the perception people have of the University, can be used as a branding asset and, most of all, impacts the lives of everyone living, learning, and working there. The Campus is a very powerful tool, one that universities worldwide have been using as a way of positioning themselves, of attracting students and faculty, and also creating synergies and relationships with companies. It shapes the relationships created inside and outside of it. As such, this research argues that universities can be key elements in generating and enabling dynamic synergies, promoting the presence of students, academics, and learning spaces in urban contexts. To accomplish this, universities should preserve their spatial identity and uniqueness, while guaranteeing the existence of adequate places for all learning related activities and embodying inclusion and sustainable development, promoting encounters and interaction. These two needs, for inclusion and livelihood while safeguarding some privacy coexist creating some tension for all campus users. With this issue in mind, this paper explores an analytical framework for university campuses within urban fabrics, understanding the different types of urban insertion and connections established with local and regional players, and exploring the dichotomy between closeness centrality and betweenness centrality, as variables than can be used to balance the tension between integration and privacy that affects university campuses and academic communities worldwide. Four compact university campuses that host similar functions are used to test the methodology: Simon Fraser University in Burnaby, Canada; Aalto University in Espoo, Finland; MIT in Cambridge, MA, USA; and Yale University, in New Haven, USA. This paper relies on syntactic analysis to provide deeper information and some clarification on the university location and accessibility within the urban fabrics.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".