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Record W2908838564

L’appropriation locale de la grande vitesse ferroviaire en Europe

2012· article· fr· W2908838564 on OpenAlexaff
Sylvie Delmer, Valérie Facchinetti-Mannone, Philippe Ménerault, Cyprien Richer

Bibliographic record

VenueLillOA (Université de Lille (University Of Lille)) · 2012
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsCenter for Northern Studies
Fundersnot available
KeywordsLocale (computer software)HumanitiesEnvironmental scienceComputer scienceArtOperating system
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.195
Teacher spread0.184 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2012
Admission routes1
Has abstractyes

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