L'appropriation locale de la grande vitesse ferroviaire en Europe: Les pouvoirs locaux et régionaux face au choix de tracé et de localisation des gares
Bibliographic record
Abstract
Cet article s’interesse a « l’appropriation locale » de la grande vitesse ferroviaire, c’est a dire a la capacite des acteurs locaux et regionaux a inflechir l’inscription spatiale des LGV concues a une echelle plus large (nationale et europeenne). La methodologie s’appuie sur trois dimensions issues de l’analyse des reseaux –la morphologie, la topologie et le service–, completees par l’observation des projets urbains. La configuration spatiale du reseau ainsi decrite, est comprise comme le resultat de l'expression d'un ensemble de pouvoirs qui temoigne de differents modes d’appropriation territoriale de la grande vitesse. Les reflexions portent sur quatre agglomerations en position intermediaire sur les liaisons a grande vitesse : Lille et Nancy-Metz en France, Anvers en Belgique et Saragosse en Espagne. Ces espaces d'intermediation entre metropoles a forte visibilite internationale, se trouve face a un enjeu commun de positionnement sur un reseau qui, par nature, accentue la differenciation spatiale. Cette contribution identifie des trajectoires differenciees d’appropriation de la grande vitesse ferroviaire qui incite a privilegier la definition de cadres d'analyses permettant de faire ressortir les singularites locales plutot que la recherche de loi d'organisation de l'espace.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".