Building a digital economy (the case of BRICS)
Bibliographic record
Abstract
The main direction of our research on building a digital economy includes the introduction of blockchain and cryptocurrency in the BRICS countries; advantages, obstacles, and prospects of the digital economy; the impact of robotization on the economic development of countries. The digital transformation of the economy of the BRICS group can be facilitated by the use of blockchain technology. The study identified the main advantages, threats and directions for the creation and use of a new cryptocurrency (BRICScoin) and blockchain technology by the BRICS countries. The digital economy is on the agenda around the world today, it is a new paradigm for the development of countries’ cooperation, and can become a driver of their economic growth. On the basis of the analysis, the advantages, obstacles and recommendations for the development of digital transformation in the BRICS countries were identified. Research in the development of robotics has revealed the benefits and challenges of this process. The use of a mathematical model made it possible to conclude that the growth of an existing fleet of industrial robots in the country affects the growth of its economy. The further development of robotics in the country will help increase its economic potential, product quality and export of innovative high-tech products.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".