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Record W4414290482 · doi:10.4324/9781315758886-13

Conclusions

2025· book-chapter· en· W4414290482 on OpenAlexaboutno aff
Patrizia Ingallina

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Urban Networks and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaMandateState (computer science)Urban planningPerspective (graphical)Third worldPoint (geometry)

Abstract

fetched live from OpenAlex

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;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;s mandate to halt the creation of new clusters in the capital and the metropolitan government&s;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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.374
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3740.208

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.

Opus teacher head0.014
GPT teacher head0.277
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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