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Record W3196365725 · doi:10.4000/brussels.5678

The Brussels Smart City: how “intelligence” can be synonymous with video surveillance

2021· article· fr· W3196365725 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.

Bibliographic record

VenueBrussels Studies · 2021
Typearticle
Languagefr
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesCentralisationPublic administrationEthnologySociologyLawPhilosophy

Abstract

fetched live from OpenAlex

En retraçant le processus de mise à l’agenda ayant conduit à l’appropriation du concept de Smart City par la Région bruxelloise en 2014, cet article interroge les choix de politiques publiques visant à faire de Bruxelles une « ville intelligente ». Tandis qu’un des objectifs théoriques de la Smart City consiste à vouloir décloisonner l’action publique en favorisant la réalisation de politiques transversales par le recours aux technologies, force est de constater que les politiques bruxelloises en la matière restent essentiellement cantonnées aux compétences de l’organisme technique régional. Cet article tente ainsi de comprendre pourquoi aucune politique transversale en matière de mobilité – secteur habituellement prioritaire pour ce type de projets – n’émerge dans le cadre de la Smart City bruxelloise, tandis qu’une politique sécuritaire s’impose comme son principal chantier. La centralisation de la vidéosurveillance régionale constitue l’unique politique du projet Smart City bruxellois parvenue à dépasser le cloisonnement institutionnel régional. Cet article démontre par conséquent comment, à Bruxelles, l’organisation politico-institutionnelle régionale influence les choix de politiques publiques.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.245
Teacher spread0.208 · 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