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Record W7039445946

Méthodologie d'évaluation du partage spatiotemporel de la rue

2021· other· fr· W7039445946 on OpenAlexaboutno aff

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2021
Typeother
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublicsESPACETown hallPublic investment
DOInot available

Abstract

fetched live from OpenAlex

RÉSUMÉ: De plus en plus de documents de planification, au Québec et ailleurs, contiennent des objectifs relatifs à l'amélioration du partage de la rue entre ses divers usagers : piétons, cyclistes, usagers du transport collectif, automobilistes, notamment. Plusieurs démarches visent également à rétablir un équilibre entre les différentes dimensions de la rue : la rue comme lien, c'est-à-dire comme espace dédié à la mobilité, mais aussi la rue comme lieu, c'est-à-dire comme espace d'activités, ainsi que la rue comme espace accueillant des fonctions environnementales. Ce partage de l'espace de la rue est le produit d'un arbitrage politique, administratif et technique entre les divers modes, arbitrage sous-tendu par des principes implicites ou explicites d'équité. De plus, le partage de l'espace urbain est changeant : il varie selon l'heure de la journée, le jour de la semaine et la période de l'année. Toutefois, peu de méthodes d'évaluation et d'indicateurs de suivi ont été proposés pour assurer la mise en oeuvre des objectifs publics en matière de partage de la rue. Alors même que les pouvoirs publics investissent des sommes colossales dans le réaménagement des infrastructures urbaines dans le but d'en améliorer le partage, il n'existe toujours pas de mesures à grande échelle pour étudier le caractère équitable de ce partage et pour en assurer le suivi dans le temps. Quelques méthodes ont été proposées, mais toutes ces méthodes dépendent de relevés effectués sur le terrain ou encore d'une classification manuelle des surfaces à l'aide de photos satellites. Il n'existe ainsi, à l'heure actuelle, aucune méthode systématique pour effectuer le diagnostic du partage de l'espace de la rue à l'échelle d'un territoire urbain. Le partage de l'espace urbain reste donc un concept difficile à définir, à mesurer et à évaluer dans le temps. ABSTRACT: More and more planning documents, in Québec and elsewhere, aim to reallocate street space between the various users of the street, namely pedestrians, cyclists, public transit riders and motorists. Several approaches also aim to restore a balance between the different dimensions of the street: the street as a link, i.e., a space dedicated to mobility, but also the street as a place, i.e., an activity space, as well as the street as a space dedicated to environmental functions. The allocation of street space is the product of a political, administrative and technical arbitration between the various modes and uses of the street, an arbitration underpinned by implicit or explicit principles of justice. In addition, the allocation of urban space is not fixed: it varies according to the time of day, the day of the week and the time of year. However, few evaluation methods and monitoring indicators have been proposed to oversee the realization of public goals in terms of street space reallocation. Even though authorities invest colossal sums in the redevelopment of urban streets, there are still no generalizable measures to study the equitable nature of these reallocation operations and to ensure their fairness or to monitor their evolution over time. A few methods have been proposed, but they depend on surveys carried out in the field or on a manual classification of surfaces using satellite photos. At the present time, there is no systematic method for carrying out the diagnosis of the sharing of street space at a city scale. The equity of street space allocation therefore remains a difficult concept to define, measure and evaluate over time.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0070.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.003

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 head0.024
GPT teacher head0.259
Teacher spread0.236 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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