Ville intelligente ou vies intelligentes
Bibliographic record
Abstract
Si les technologies avancées comme l'internet des objets (IoT), l'intelligence artificielle (IA) et les infrastructures de données offrent des solutions pour améliorer a priori la gestion et la qualité de vie urbaines, elles soulèvent également des défis et des risques majeurs. Cet ouvrage explore les dimensions constitutives de la ville dite intelligente sous deux angles singuliers : les nouveaux usages stimulés par les innovations et les besoins que ces dernières viennent combler (mobilité, alimentation, culture, tourisme, habitat, etc.). Issu notamment des travaux de recherche de la Chaire internationale CitUs en collaboration avec les villes de Montpellier et de Montréal, il explore les impacts de la transformation numérique, écologique et sociale sur la gestion urbaine et la vie des habitants. En adoptant une approche centrée sur l'humain, cet ouvrage montre comment les infrastructures numériques pourraient améliorer la vie quotidienne tout en répondant aux principaux enjeux d’une urbanisation croissante. Il propose une lecture essentielle pour engager la communauté académique dans des villes en transition et aider la décision publique et privée dans l’élaboration de nouveaux projets territoriaux alliant innovation et bien-être individuel et collectif.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".