Deeply divided along aid lines? Chinese loans, Cameroon and Anglophone marginalisation
Bibliographic record
Abstract
Chinese development financial assistance goes into Africa mainly as loans but little is known about recipient sub-national distributive patterns, despite the potential of uneven allocations for inequitable development and conflict outcomes. Focusing on Cameroon, which is among the largest recipients of Chinese loans and is challenged by secessionist unrest, we explore if sub-state allocations favour the president’s birth region. We geo-locate Chinese loan-funded projects in Cameroon from open sources, fieldwork observations, and reviews of Sino-Cameroon agreements, and conduct interviews to gauge the influence of selected projects on voting in Cameroon’s 2018 presidential elections. We find evidence of more Chinese loan-funded projects reaching the president’s birth region and motivating pro-incumbent votes. Conversely, we find less Chinese loan-sponsored projects in the secessionist Anglophone regions, providing an empirical basis for proposing adjustments to the geopolitical configuration of Chinese development assistance to Cameroon for more equitable and auspicious outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".