Trade flows analysis in textile sector between China, EU-28 and selected group of countries (2008-2014). A gravity model
Bibliographic record
Abstract
In an increasingly globalized world, political and economic relations between countries are of great importance.This article shows the existing business relationship between China and the European Union from 2008 to 2014.Thus focusing on the textile sector, we want to analyze the impact that have exports and Chinese trade flows on the countries of the European Union during the period of economic crisis that has affected the world economy.It is analyzed by a gravitational econometric analysis, which variables are the most important and which ones have more value in the field of study we refer.Furthermore, subsequently, and in order to make a more accurate estimate, another econometric model will be estimated including, apart from the 28 countries of the European Union, the United States, Canada, Brazil, South Africa, Australia, Argentina, Mexico, Morocco and New Zealand, thereby increasing the number of observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".