Hope, Politics and Risk: The Case of Chinese Dam in Nigeria
Bibliographic record
Abstract
The rise of Chinese infrastructure investment in Africa has raised a set of questions about whose development agendas are being fulfilled by such projects, where the power lies in these negotiations, and how local communities are impacted by the projects. Current assumptions see China as holding the power in these relations and that its state-backed transnational corporations unilaterally get their way. This paper challenges these simplistic assumptions by examining the case of a ‘failed’ Chinese project - the Zamfara Dam in Northern Nigeria – and in doing so makes a case for the role of African political agency in brokering Chinese engagement. The dam project was initiated in 2008 between the Zamfara State government and the China Geo-Engineering Corporation; funding was supposed to come from the Chinese ExIm Bank. After the initial assessment and community consultations that spanned three years, the project failed to take off. Primary data is used to understand the process of failure and shows that the dam was initiated based on political expediency rather than the actual drive for development. It was brokered between the elites of China, Nigeria and Zamfara state and so failed to gain wider legitimacy and accountability. Also, in the drive to see the project initiated statutory shortcuts were taken. Critically, consultation was not broadbased even among the state government officials and the communities. The initiation of the project did not follow the laid down procedure of the Federal Ministry of Water Resources. Given that largely political factors played a significant role in the failure of the project, it is suggested that motivation for and implementation of development projects of this nature should transcend political whims and caprices of politicians and ensuring more transparency and broad consultation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".