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Record W2985377260 · doi:10.1111/cag.12573

Pourquoi seules les villes sont‐elles qualifiées d'intelligentes? Un vocabulaire du biais urbain

2019· article· fr· W2985377260 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2019
Typearticle
Languagefr
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversité du Québec en OutaouaisMcGill University
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Depuis 2010, le terme « ville intelligente » (ou « smart city » en anglais), terme vague qui renvoie à l'intégration croissante des technologies de l'information aux processus de gestion urbaine et, parfois, aux pratiques sociales et communautaires associées, est beaucoup utilisé. Or, l'adjectif « intelligent » n'est associé qu'aux villes: par implication les non‐villes (soient les régions rurales ou périphériques) ne sont pas intelligentes. Dans cet article, nous décrivons comment le terme « ville intelligente » est utilisé et montrons que des processus semblables à ceux qui rendent les villes « intelligentes » se déploient en dehors des villes. Réserver l'adjectif « intelligent » aux villes reflète donc un biais, soit le même biais qui n'associe l'innovation et la créativité qu'aux villes. En tant que géographes, nous avons pris conscience de nos biais disciplinaires coloniaux et sexistes. Cet article démontre qu'un biais urbain demeure présent et il suggère d'en prendre conscience afin de modifier notre manière de penser les différents territoires.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.007
Science and technology studies0.0010.004
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.178
Teacher spread0.170 · 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