The Differentiation of Products and Opportunity for Intra-Industry Exchange
Bibliographic record
Abstract
The financial crisis of the late 2000s gave rise to protectionist hints which called into question the consensus on the liberalization of world trade since the creation of the World Trade Organization (WTO) in 1995. This trend towards protectionism has taken on new magnitude with the arrival of Donald Trump as President of the United States of America. In fact, since the beginning of 2018, the American administration has carried out its threats by imposing customs duties on imports of the various products from China and the European Union. In retaliation, the countries concerned responded with restrictions on American exports to their territory. Also the rationality of the market economy, there is more and more opposed the power of emotions and impulses embodied by the populists at the head of which D. Trump, the American President. Globalization is therefore required to adapt its rules to survive. The purpose of this paper is to show that for a good adaptation of its rules, it is necessary to activate one of the most powerful levers of gains in international trade, the differentiation of products. This is a response to the exploitation of the diversification and heterogeneity of demand in terms of tastes and incomes. Because, by allowing the firm to differentiate its products to distinguish them from those of competitors, differentiation offers the opportunity to soften competition, increase profits and improve product quality.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".