Les contributions de la Chine au financement et à la réalisation des infrastructures en Afrique
Bibliographic record
Abstract
En Afrique, si les besoins en infrastructures sont incommensurables, les besoins en financement le sont tout autant. Parmi les principaux bailleurs de fonds, la Chine et ses acteurs ont la particularité de pouvoir construire en Afrique l'infrastructure qu'ils financent. Cet article explore plusieurs sources statistiques nationales, régionales et internationales pour évaluer la contribution des acteurs chinois au financement et à la réalisation des infrastructures africaines. Notre étude démontre qu’ils interviennent principalement par le biais des prestations de services, que leurs interventions ne sont pas différentes de celles des autres et qu’elles sont majoritairement orientées vers les transports en Afrique de l'Ouest. Dans ce contexte, la Chine et ses représentants reproduisent-ils un modèle de développement inégal ? Classification JEL : F5, N4, N73, O1
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".