Bibliographic record
Abstract
The purpose of the article is to determine the importance of domestic developers of the digital distribution industry for the state's economy, general trends in the development of the digital distribution market of Ukraine, and the development of mechanisms to promote the further development of domestic manufacturers.Based on an analysis of the dynamics of exports of digital goods and services between the 1st quarter of 2018 and the 2nd quarter of 2023, the indicator reached its peak shortly before the full-scale Russian invasion, when the direction brought in $2.1 billion.Since then, average exports of digital goods and services have gradually declined to US$1.7 billion, representing a drop of roughly twenty percent in quarterly IT export revenue.In order to improve the conditions for the development of digital distribution in Ukraine, the following main areas should be developed: 1) make changes in the legislative base; 2) promote the development of domestic digital distribution services.The activities of domestic representatives of digital distribution and, in addition to their business activities, so as their behavior towards citizens of Ukraine are determined.Based on the study of trends in the development of digital distribution in Ukraine, the expediency of state support for this direction was determined.Approaches to the formation of a mechanism for state assistance in the development of digital transformations in the context of increasing the sustainability of modern business models of international companies have been identified, which include political and legal levers (development of legislative acts regulating the protection of copyright for digital goods and services), economic (simplification of the taxation system for companies, that provide services for the creation and/or promotion of digital goods and services) and administrative levers (supporting the development of technoclusters in the direction of the creation and promotion of digital goods and services).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".