China and African Governance in the Extractive Industries
Bibliographic record
Abstract
This paper examines China’s role in the extractive industry sectors of sub-Saharan Africa and issues surrounding governance—particularly the maximisation of host country economic gains. China’s involvement is controversial and the focus of international debate as to the extent to which Chinese–African relationships in this key sector are not ‘win–win’ but are damaging African partner economies and political cultures. The paper’s motivation is a desire to explain more closely the growing involvement of China in sub-Saharan Africa’s extractives sector in terms of how effectively African governance works to maximise the gains accruing to China’s African partners. A central question is how far there is Chinese synchronisation with the rules, principles, norms and behavioural expectations of African partners. The study assesses the experience of the Democratic Republic of Congo (DRC). Key findings are that China’s involvement takes many forms, but is heavily influenced by its own history as well as its emerging engagement with the international development assistance system. The DRC case demonstrates that the effectiveness of African regulatory regimes is highly variable and depends on the quality of governance. Africa has extensive regulatory and normative regimes that frame the Chinese relationship. However, to maximise gains to African partners, the Chinese state and Chinese firms must strengthen policy on corporate responsibilities and practice whilst African states must strengthen the quality of governance to turn political commitments into more robust practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".