Bibliographic record
Abstract
This study examines the dynamics of Nigeria-China debt relations between 2006 and 2021, focusing on the evolution, implications, and sustainability of Chinese loans to Nigeria. Over this period, China emerged as one of Nigeria's most significant bilateral creditors, providing concessional loans primarily for infrastructure development in sectors such as transportation, energy, and telecommunications. These loans were instrumental in bridging Nigeria’s critical infrastructure gaps and fostering economic growth. However, they also led to an increased debt burden, raising concerns about fiscal sustainability, debt servicing capacity, and economic sovereignty. The research adopts a time-series design to analyse trends in Nigeria’s external debt profile and incorporates both primary data from key informant interviews and secondary data from institutional sources. It identifies the processes, terms, and conditions of Chinese loans and evaluates their economic impact, transparency, and risks of dependency. Findings indicate that while Chinese loans have contributed significantly to Nigeria's infrastructure development, the growing debt servicing obligations, coupled with a lack of transparency and over-reliance on external financing, pose challenges to Nigeria's long-term financial stability. The study recommends diversified debt sources, enhanced transparency, and strengthened debt management frameworks to mitigate risks associated with Chinese loans. It underscores the need for policy strategies that balance the benefits of international financing with the imperatives of economic sovereignty and sustainable development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".