Comment repenser et réaménager les villes africaines au XXIe siècle ? How to rethink and rework African cities in the 21st centuries?
Bibliographic record
Abstract
ArgumentaireL'explosion démographique et l'extension fulgurante des villes africaines, consécutives au phénomène d'urbanisation galopante, provoquent un accroissement rapide des besoins de la population africaine en eau, logement, emplois, espaces publics, etc. Selon les Nations Unies (2014), l'Afrique comptera 1,2 milliard d'urbains en 2050, contre 450 millions aujourd'hui ; sa transition urbaine s'effectuera à l'horizon 2035 avec 50% de sa population vivant en ville.Ce phénomène induit dans ces villes la naissance et la prolifération des quartiers spontanés souséquipés, sous-intégrés, la paupérisation, la baisse du niveau des services urbains, la dégradation de l'environnement urbain, l'hétérogénéité des armatures urbaines et une persistance des problèmes de spéculation foncière (Legay, 2011), si elle n'est pas maitrisée.A cela s'ajoutent la dégradation de la voirie urbaine, les problèmes d'assainissement, d'approvisionnement en électricité, de propagation des pandémies telles que le VIH/SIDA, le coronavirus, les changements climatiques en milieu urbain, l'insécurité, et la maintenance des infrastructures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".