LIBERALIZATION OF THE INTERNATIONAL TRADE AND ECONOMIC GROWTH: IMPLICATIONS FOR BOTH DEVELOPED AND DEVELOPING COUNTRIES
Bibliographic record
Abstract
The debate over trade liberalization is part of a larger debate that deals with the impact on the economic growth of free movement of goods, capital and labor force across borders. Most economists agree that trade liberalization could positively affect economic growth, but the differences are at what stage of development a country should open its market. The paper describes different views that form the world trade policy mosaic. Then, I show contradictions in trade policies of the developed countries while convincing poor nations to lift trade barriers, but, in the same time, they adopt anti-dumping procedures and protectionist policies on agricultural products, textiles, and steel imported from developing countries. The liberalization of trade has been pushed by international organizations mostly towards developing countries through structural adjustment loans conditionalities of the World Bank and IMF, within the World Trade Organization negotiation framework. This paper addresses the changes, in the last year or so, within international organizations regarding trade liberalization policies. There is more understanding in the world now that industrialized countries ’ protectionist trade policies are on the expense of developing countries, in particular of the least developed countries. International organizations started to shift their focus from imposing liberalization of trade in developing countries to eliminating tariff and non-tariff barriers in developed countries, especially in Quad countries—Canada, the EU, Japan, and the United States.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".