High Tech Services Diffusion Points to a New Urban Hierarchy : The Case of Large French Cities/ la Diffusion Des Services De Haute Technologie et Les Changements De la Hierarchie Urbaine
Bibliographic record
Abstract
A diffusion process approach to high technology activities' dynamics represents a good indicator of changes in urban structure and national hierarchy. The case of computing services allows us to see that the development of innovative activities constitutes a crucial factor in a city's ability to change categories and acquire metropolis status or miss the opportunity and be relegated to that of an ordinary city. Une approche basee sur les processus de diffusion des activites de haute technologie represente un bon indicateur des changements de structures urbaines et de hierarchie nationale. Le cas des services informatiques permet de voir que le developpement des activites innovantes est un facteur primordial pour expliquer pourquoi une ville peut changer de categorie et devenir une metropole plus grande, ou, par contre, peut aussi decliner pour devenir une ville ordinaire. ********** Analysing the spatial diffusion of innovative activities is becoming a standard way of treating the question of localisation dynamics. Principles of contact and hierarchical diffusion have been verified in numerous sectors such as agriculture, business, services and high technology activities. Activities linked to innovation follow the standard frameworks of diffusion models, as defined by Hagerstrand (1953-67) and improved or adapted by numerous authors. (2) It is possible to extend these theories to a large number of activities linked to knowledge only as well as high skilled employment (see, as an illustration, Michels (2000, 2001) on services for firms). Conversely, they can also be applied to justify the important role played by poor social and economic conditions in the absence of diffusion (see, as an illustration, Liefooghe (2002) on the role played by legacy in old mining regions in relation to the observed absence of the diffusion of services for firms). Most of these studies are searching for a synthetic model, or are analysing location dynamics on a one-model vision basis. These studies are quite interesting, but their conclusions reach an intersecting point between a standard perspective and a very interesting line of research. Actually, it is possible to use models in another way, as we shall try to demonstrate. The core idea of this paper is that standard diffusion processes do not just constitute a way of describing location dynamics. They also allow us to understand, compare and rank the structures as represented by the various metropolitan areas. Typically, metropolises and towns are ranked in accordance with static statistic criteria (such as population and economic growth). These parameters form the basis of a range of classifications in terms of levels of complexity (see Chevalier 2000; Bonnet 2000; Polese and Tremblay 2005). (3). But if we use the diffusion based hypothesis as a new parameter, the diffusion process may point to different urban structures as well as different stages in the evolution of the city, and most importantly, to different abilities in integrating an innovative system and playing a leading part in the development of its region. The main point concerns the ranking of cities. Can we observe large differences between the standard urban hierarchy and positions as defined by diffusion processes? According to the principles of diffusion theory, we should observe important differences between the top of the urban hierarchy and the lower levels in relation to factors such as the importance of diffusion, the timing of the when the process began and elements regarding the spreading across the region. The most advanced city would be the main metropolis, and thus constitute the diffusion pole branching out towards the rest of the system. Then, large cities should have reached an intermediate stage, and so on. If this hypothesis is verified, it would demonstrate the positive feedback between new knowledge activities and the reinforcement of established organizations. …
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".