THE DEVELOPMENT TRENDS OF E–COMMERCE SERVICES IN THE UNITED STATES
Bibliographic record
Abstract
The article deals with the latest trends in US trade in electronic services, in particular audiovisual services, computer services and data processing services, telecommunication services. Since 2007 trade of audiovisual services has been the most significant in theUSe-services export. The largest consumers of these services are the European Union, Asia and the Pacific region (the main consumers areChinaandIndia) and Central and South America (BrazilandArgentina). Among the countries, the main importers of American audiovisual services are theUK,CanadaandGermany. The main share of audiovisual services is occupied by film distribution and streaming media. In theUSAaudiovisual services are imported by theUK,Brazil,Mexico,CanadaandArgentina. For several years there is a deficit in the trade turnover of computer services in theUnited States. The main importers of these services from theUnited Statesare theUnited Kingdom.Canada,Switzerland,India,Germany. TheUSA, in turn, uses computer services fromIndia(47%),Canada,Ireland, theUKandGermany. The American telecommunications market is about a quarter of the world's, so theUSAis the largest national market for this type of service. The importing countries of theUStelecommunications services are theUnited Kingdom,Mexico,India,Canadaand theNetherlands, and the main export consumers areBrazil,Argentina, theUnited Kingdom,VenezuelaandCanada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".