European Union international trade in services. Analytical aspects 2003-2007, 2009 edition
Bibliographic record
Abstract
Collectively these countries recorded a surplus of €114.3bn(€92.6bn in 2006).However, this total amount conceals differences in individual countries.In 2007, the EU recorded a surplus of €84.1bn, compared to €68.5bn in 2006.Other countries persistently running surpluses were the USA, Switzerland and Turkey.The highest deficit was recorded by Japan (€-15.5bn),closely followed by South Korea (€-15.0bn)and Russia (€-14.5bn).Other countries with significant deficits were Canada, Brazil, Thailand, China, and Mexico.2 Transportation covers all transportation services that are performed by residents of one economy for those of another and that involve the carriage of passengers, the movement of goods, rental of carriers with crew, and related supporting and auxiliary services.3 Travel covers primarily the goods and services acquired from an economy by travellers during visits of less than one year to that economy.4 Other services comprise: communication services, construction services, insurance services, financial services, computer and information services, royalties and license fees, other business services, personal, cultural and recreational services and government services.5 Intra-EU transactions are excluded from this analysis since the EU is treated as a single entity.Other services 49 % Transport 23 % Travel 28 %
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.063 |
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".