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Record W1640686401 · doi:10.3917/flux.083.0051

Les Syndicats Mixtes de transport de la loi SRU: un outil pour la gouvernance interterritoriale des mobilités?

2011· article· fr· W1640686401 on OpenAlexaff
Cyprien Richer, Sophie Hasiak, Nicolas Jouve

Bibliographic record

VenueFlux · 2011
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsMinistère des TransportsCenter for Northern Studies
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Résumé En 2000, la loi Solidarité et Renouvellement Urbain (SRU) a encouragé le développement de syndicats mixtes de transport dits « SRU » sous une forme institutionnelle nouvelle pour favoriser la coopération entre Autorités Organisatrices de Transport (AOT) de différents niveaux. Une dizaine d’années après, cet article propose d’étudier ces structures syndicales pour tirer un bilan des situations existantes et ouvrir des débats sur l’application locale de la réforme. Devant le défi croissant de faire interagir les politiques urbaines au-delà des clivages sectoriels ou spatiaux, les syndicats mixtes de transport SRU constituent-ils un véritable enjeu pour une nouvelle gouvernance des mobilités, susceptible de mieux prendre en compte « l’interterritorialité » des problèmes contemporains? Le premier bilan de l’étude des syndicats mixtes SRU témoigne de la souplesse de l’outil face à la diversité des situations locales. Ces syndicats apparaissent comme un fragile rouage de l’architecture interterritoriale, malgré l’étendue des enjeux qu’ils portent en matière d’intermodalité et de « mobilité durable ». Il convient cependant de relativiser les interprétations par rapport aux récentes évolutions et créations de syndicats SRU non intégrées dans cette étude.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.273
Teacher spread0.226 · 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 designObservational
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

Citations6
Published2011
Admission routes1
Has abstractyes

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