Chinese Currency Devaluation and the Economic Implications for Nigeria
Bibliographic record
Abstract
The decision of Chinese government to devalue the Yuan has attracted serious condemnation by majorly developed economies, despite the government position that the devaluation was aimed at aligning the Yuan with the market rate. The general argument is anchored on the notion that the devaluation was a strategy to increase China's share of global trade by making its goods cheaper in the international market. This thinking is influenced by the long standing Mundell–Fleming model, which aligned with the theory that competitive devaluation is detrimental to the world economy, because of beggar-thy-neighbour welfare effect. The situation is compelling other countries to respond by improving their balance of trade in response to China tactics. The inability of developing economies to respond to this global trade war could be attributed to factors such as colonialism, presence of agency of restraints, complementarity among developing countries, and other institutional rigidities such as technological deficiency, infrastructure deficit, and commodity based exports, among others. Chinese currency devaluation has impacted meaningfully on the Nigeria economy given the fact that Nigeria maintains strong economic ties with China. The paper argues that for developing economies to effectively respond to competitive devaluation, they must close their borders to certain goods, improve infrastructure, prioritize technological transfer, embrace value-added production, and eliminate institution rigidities that hinder the ease of doing business.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".