Passer par la fenêtre périurbaine quand la porte urbaine est fermée : des régimes (péri)urbains pour le développement logistique des métropoles européennes ?
Bibliographic record
Abstract
The considerable logistics development of metropolises catalyzes new economic functions in the suburban spaces. However, this economic dynamic depends on several local public policies. In order to analyze these policies, the urban regime approach appears to us particularly heuristic. To do that, we will support our communication with four study cases: Mitry-Compans, Val Bréon, Sénart in the Paris Region and Venlo in the Netherlands. They show different types of coalition whose main variables are the form of the economic activity strictly speaking, such as its local embedding, and the regime of the production of logistics spaces. These two elements structure the relationship between the economic sphere and the public policies. Then, the shift of the analysis towards suburban spaces and peripheral economic activities shed a new light on the concept itself and on the issue on its transfer from American cities to European cities. Indeed, within the metropolises, the fringes where the institutional density is lower (Lorrain, 2011) seem to entail to 'governance discontinuities' (Borraz, Le Galès, 2010) leading to deeper participations of private firms within public policies. When the door of the European city-centers is locked, would urban regimes enter through the window of the suburbs and the peripheral economic activities?
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".