Trade War and Economic Development: The Case of Some Selected Major Economies.
Bibliographic record
Abstract
Globally, trade liberalization is seen as a major source of economic development. In spite of this, United States, China and Canada have recently imposed different forms of trade restrictions on selected products from each other’s countries, European Union and Mexico have also threatened different countermeasures to the US’s trade war. These are capable of causing severe negative economic consequences to global trade if not halted. Consequently, this paper examines the impact of trade war on economic development of six selected major economies of the world. The study proxied trade liberalization by import and Export volumes, economic development is measured by Gross Domestic Product (GDP) per capita (constant 2015 US $) and Misery Index (MI) measures economic distress. The study adopted stratified random sampling method in selecting six countries (Australia, United States of America, United Kingdom, Brazil, China and Nigeria) and employed descriptive analytical techniques on these time series data. The study found that, the trends of these indicators were volatile during normal economic circle and even more during major economic shocks like the financial crisis of 2009, the Brexit of 2016 in the UK, the United States’ presidential election campaign rhetoric of president Trump of trade protectionism, deregulation and tax cut and his trade war pronouncements of 2016. The study recommended that no country should unilaterally impose trade restrictions on imports from others countries since it can degenerate into a full-blow trade war. World Trade Organization should ensure that trade disputes among member countries are settled as quickly as possible.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".