The Brussels Smart City: how “intelligence” can be synonymous with video surveillance
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".