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Record W2955093405

"En ville, sans ma voiture ?" Evaluation du 22 septembre 1999 : résultats et analyses

2007· preprint· fr· W2955093405 on OpenAlexaff
Erwann Fangeat, Françoise Mermoud, Thierry Gouin, Sylvie Paillard, Louise Walther, Jean‐Louis Couderc, Fabrice Hasiak, Bernard Castets, Patricia Fréret, Vincent M. Bruno, Sidonie Guénin, Gérald Chirouze, Nathalie Fürst, Maxime Petit Jean, Jean-Marie Guidez

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2007
Typepreprint
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsCenter for Northern Studies
Fundersnot available
KeywordsHumanitiesPolitical scienceGeographyArt
DOInot available

Abstract

fetched live from OpenAlex

A partir des enquêtes, des observations et des entretiens réalisés dans une douzaine de villes françaises et plusieurs villes étrangères, les principaux enseignements de la journée sont les suivants :- l'opinion publique locale est toujours très favorable à ce type d'opération (plus de 80 %), à l'exception des commerçants ; ce niveau de satisfaction est élevé dans tous les pays d’Europe, même s’il l’est un peu moins en Allemagne (autour de 70 %).- la fréquentation du centre ville est d’un niveau équivalent à un jour ordinaire, avec quelques disparités dans certaines villes selon les types de commerces ou de services, et les grandes surfaces périphériques n’ont pas plus de clients qu’un jour ordinaire;- l’usage de la bicyclette et de la marche s’accroît ainsi que la fréquentation des transports collectifs ;- en matière d’environnement, la baisse du niveau de bruit et surtout le changement d’ambiance sonore est apprécié par les citadins. On constate une baisse de pollution dans les périmètres réservés. Par contre, cette journée a peu d’influence sur les niveaux de pollution de fond.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.050
GPT teacher head0.319
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2007
Admission routes1
Has abstractyes

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