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Record W2795097441 · doi:10.4000/netcom.2756

Les plateformes numériques révolutionnent-elles la mobilité urbaine ?

2017· article· fr· W2795097441 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNetcom · 2017
Typearticle
Languagefr
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesLocale (computer software)Political scienceArt

Abstract

fetched live from OpenAlex

Les TIC, en particulier le smartphone, permettent d’accéder à de nouvelles opportunités de déplacement permises par des plateformes numériques qui mettent en relation des passagers avec des chauffeurs occasionnels. Uber, une des plateformes numériques les plus connues, est aujourd’hui présente dans plus de 700 villes à travers le monde. L’implantation de son service est controversée à l’échelle locale, notamment en raison de la concurrence jugée déloyale avec l’industrie des taxis. S’appuyant sur un corpus de plus 350 articles de presse, cet article analyse le discours local de l’arrivée d’Uber à Paris et à Montréal. Malgré des contextes locaux différents, les résultats soulignent de fortes similitudes dans les discours : Uber est une nouvelle offre de mobilité qui satisfait une demande tout en incitant la puissance publique à se positionner, notamment face à l’industrie du taxi.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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