Bibliographic record
Abstract
According to recent data, the business environment in Georgia has significantly improved and doing business has become much easier, as evidenced by official statistical information, in the World Bank Group’s annual report “Doing Business” from the website. According to 2024 data, Georgia ranks 7th out of 190 countries in terms of ease of doing business and has maintained the same position for 5 years, which is a good indicator for our country. In January-August 2025, Georgia’s foreign trade turnover in goods (excluding undeclared trade) amounted to 16,252.0 million USD, which is 8.8 percent more than in the corresponding period of the previous year. Of this, exports amounted to 4,448.0 million USD (increased by 6.7 percent), and imports amounted to 11,804.0 million USD (increased by 9.7 percent). The negative trade balance in January-August 2025 amounted to 7,356.0 million USD, which is 45.3 percent of foreign trade turnover. According to a study published by the European Business Association (EBA), the main challenge for businesses in Georgia is political instability, which the organization names as the main obstacle to doing business among the 200 European companies surveyed. 76.8% of businesses surveyed name political instability as the main challenge, followed by the shortage of workers at 65.9%. The ineffective judicial system is in third place at 58%. The study also shows that the impact of the shortage of qualified labor on GDP loss is 26% annually. In 2024, 41.7 percent of the total number of employees was in large businesses, 21.5 percent in medium-sized businesses, and 36.9 percent in small businesses. During the reporting period, the total number of employees amounted to 764.2 thousand people, 3.0 percent more than the previous year. By type of activity, relatively high salaries were recorded in the following sectors: Information and communication; Financial and insurance activities; Construction; Professional, scientific and technical activities. The goal of our country should be to raise the level of awareness of citizens. A developed society is the basis of a strong private sector. Considering all this, I would like to draw attention to the following conclusions and recommendations: Creating favorable conditions for business requires the state to ensure economic and political stability. A strong economy and stable business development depend on increasing domestic production to meet internal demand. One of the key challenges in Georgia’s labor market is the shortage of skilled professionals alongside an excess of unqualified labor, which calls for a systematic solution. To build a viable business environment, it is essential to develop innovative infrastructure, support the creation of tech parks, startup hubs, and business incubators, and ensure their accessibility across the regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".