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
This book presents international experiences of territorial strategies and urban projects in which universities have played a major role over the past fifteen years, through spatial planning and within a multiscalar approach. This multiscalar approach constitutes the book's first originality, illustrating the complexity of certain cases (such as New York, London, or the Greater Paris Metropolis) by highlighting the significant interconnections between spatial and institutional scales. The second innovative aspect lies in the selection of case studies, some of which are addressed for the first time in international literature (Benguerir in Morocco, Bergamo in Italy, Grenoble, Lille, Marseille, and Lyon in France, and Hanoi in Vietnam), while others are still little known from the perspective of university planning and its (political and economic) role in major metropolitan areas (Greater Paris Metropolis, Seoul). A third interesting point lies in the opportunity to directly compare the current state of Occidental well-established world metropolises such as New York and London with that of the Greater Paris Metropolis, which is still in development. The case of Montreal, the leading metropolis of Quebec, is also noteworthy, as it stands at the crossroads of two cultural influences—Anglo-Saxon and French-inspired—while striving to carve out a third path of its own. Finally, Asia is well represented by China, whose advancements in university planning and development are particularly illustrative; by Southeast Asia, with Hanoi engaging in its own “race for innovation” through large-scale university projects; and by South Korea, where Seoul finds itself caught between the national government's mandate to halt the creation of new clusters in the capital and the metropolitan government's ambition to continue fostering cluster development within the city. All these chapters have been designed to raise questions, provoke reflections, and develop a perspective on the ongoing evolution of territorial strategies that leverage universities and the research conducted within them as a means to drive economic growth and increase productivity. These strategies also converge toward an image of the city as a knowledge hub, facilitating relationships between universities, businesses, and city users. This is largely due to the preference for locating university headquarters within cities rather than in isolated, suburban campuses, which are often far removed from urban centers. The well-known image of the “knowledge city” frequently appears in territorial development strategies, even if it is not always explicitly mentioned by urban planning decision-makers. While this image is central, it is neither interpreted nor applied in the same way everywhere, as contexts vary significantly. Let us recall the notion of “context,” both spatial and social, as defined by Roncayolo (1996): First, the spatial context … The discovery of the logic behind urban forms, the relationship between scales, and the importance of inherited frameworks (…) However, context cannot be reduced to a kind of evolving cartography of spatial inscription or volumetric reading. (…) The adjustment of techniques, the social distribution of labor, power, and even representations have always been established between forms and societies. 1
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) | 0.004 | 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