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Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle