Income Effects in Global Value Chains Driven by EU Exports
Bibliographic record
Abstract
This chapter provides an overview of the main trends and patterns in income effects in global value chains driven by EU exports during the period 1995-2011. It makes an extensive use of indicators illustrating the relationship between trade, employment and income (expressed as value added) for the EU as a whole and for each EU Member State using the World Input-Output Database (WIOD) as the source for the data. The indicators relate to the EU's exports to the rest of the world with a geographical breakdown of the data that includes the 27 EU Member States (Croatia was not yet a Member State in the period covered by this analysis), Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Russia, South Korea, Taiwan, the United States of America, and an aggregate “Rest of the World” region.\nThese data allow to examine for the first time the evolution of the income flows supported by EU exports including time after the outbreak of the global financial crisis. Furthermore, the current analysis offers more specific insights into some key EU bilateral trade relationships in terms of the income flows that they support.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".