Конкурентоспроможність українських підприємств на міжнародному ринку ІТ-аутсорсингу
Bibliographic record
Abstract
In this paper a study of the definition of the competitiveness of Ukrainian enterprises on the international market of IT outsourcing is made. The current state of the international outsourcing market is analyzed, and the resulting conclusion is that even slight growth during crisis indicates high efficiency of the industry and demand for services of the outsourcing market. It has been determined that the largest consumer of business process outsourcing services is North American market. (USA, Canada). The second in importance consumer market for this type of services is the Western Europe region. The third largest customer of outsourcing services is Japan. The features of development of IT outsourcing on the Ukrainian market are studied. It was determined that, as of right now, the Ukrainian market of software development services and IT outsourcing is the largest one in Central and Eastern Europe. A significant number of highly skilled IT professionals provide the reliability of growth of the industry and focus on providing high quality IT services on the global market. It is proved that Ukrainian enterprises have significant competitive advantages on the international market of IT outsourcing.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".