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Record W7120241836 · doi:10.60659/geotrope.2.2025.16

Abidjan à l’épreuve des effondrements d’immeubles

2025· article· W7120241836 on OpenAlexaboutno aff
Amadou COULIBALY; Abou DIABAGATE; Somilan Venceslas TOURE

Bibliographic record

VenueGEOTROPE · 2025
Typearticle
Language
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyUrban districtQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

La ville d’Abidjan connaît une recrudescence des effondrements d’immeubles, symptôme manifeste d’un dysfonctionnement de la gouvernance urbaine et de l’adoption de pratiques de construction inappropriées. Cette étude mobilise une méthodologie mixte articulant approches qualitatives et quantitatives : observation directe, entretiens semi-directifs, questionnaires, ainsi qu’analyses de tableaux statistiques et de cartes réalisées à l’aide de logiciels spécialisés (Excel et QGIS). Les résultats mettent en exergue une concentration notable des effondrements dans les communes très peuplées telles que Yopougon, Abobo et Treichville. Les multiples causes identifiées sont : l’usage de matériaux de mauvaise qualité, les défaillances techniques, le non-respect des normes de construction et la corruption institutionnelle. Les conséquences, quant à elles, sont particulièrement lourdes avec les pertes en vies humaines, les traumatismes psychologiques, l’insécurité résidentielle, la précarisation économique et le renforcement des inégalités sociales. Ce phénomène témoignant des défaillances structurelles de la construction des immeubles révèle la vulnérabilité croissante des citadins face aux risques urbains et appelle à une réforme en profondeur du secteur de la construction en milieu urbain. Mots-clés : Abidjan ; effondrement d’immeubles ; construction ; vulnérabilité urbaine

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 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, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.059
GPT teacher head0.323
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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