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
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".