Paradigm of a Country Competitiveness Under Conditions of Digital Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article discusses rating of Ukraine and its neighboring countries by the Global Index of competitiveness of countries.It analyzes dynamics of alterations of indices of Belarus, Hungary, Moldova, Poland, Russian Federation, Romania, Slovak Republic and Ukraine in the ratings of countries by the level of digitalization of economy.It presents a polygon model of competitiveness of countries by the indices of competitiveness of digital economies.The research suggests the researched to be focused on problematic aspects, especially, on those causing their low indices in digital economy.Poland should pay its most attention to solve problems connected with the lack of labor, automation of industrial operations and factor of virtual reality.Moldova is recommended to apply the instruments of Gig-Economy with their ability to change the general character of employment.All suggested recommendations for improvement of ratings lie in improvement and unification of legislative basis for raising cyber security as well as the level of readiness of centralized bodies to react adequately on cyber attacks and cyber incidents on the national level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it