Chinese Investments in the African Mining Sector
Bibliographic record
Abstract
Chinese investment in the African mining sector has increased tremendously over the past couple of years. Sino-Africa bilateral investment treaties (BITs) and multilateral cooperation have been used as the governance framework for these investments. On the surface, China–Africa relations appear to be a win-win situation as each party relies on the other. However, in the economic realm, these relations have generated a lot of controversy. In particular, questions have been raised with respect to the fairness and transparency of China–Africa economic engagements and interdependence. In the face of this growing interdependence, the potential for disputes is real. Already, some Chinese investors have had misunderstandings with a few African governments. No dispute has been adjudicated yet. However, as disputes are increasingly becoming likely, this chapter examines the dispute settlement mechanisms in the context of the increasing Chinese investments in the mining sector using some Sino-Africa BITs. It examines the extent and forms of Chinese investments in the development of African countries’ mineral resources through the lens of the China Policy on Africa. It examines the utility of FOCAC’s China–Africa Joint Arbitration Centres as potential mining dispute resolution fora. The chapter observes that inadequate arbitral expertise, corruption, and inefficient and ineffective judicial processes are major challenges facing African countries regarding the protection of investments. The volatility of the mining sector further compounds the situation. The chapter argues that because of divergent legal traditions and complex legal pluralism, there is no uniform African approach to the protection of investments. It recommends that China–Africa mining dispute settlement mechanisms should avoid the shortcomings of investment arbitration and should be flexible, open to constant reforms and innovations. It recommends more transparency and a reconsideration of the existing Sino-Africa BITs to address contemporary issues of human rights, sustainable development, and social and environmental governance. It suggests the adoption of the approach adopted in some new-generation BITs such was the Nigeria–Morocco BIT 2016 and the Canada–EU Trade Agreement’s investment chapter.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".