Foreign economic activity of the Kabardino-Balkarian Republic in the second half of the 90s of the XX century
Bibliographic record
Abstract
Since the mid-90s of the twentieth century, the authorities of the Kabardino-Balkarian Republic have increased overseas economic activity as a means of overcoming the economic crisis. The positive results of the activities of the authorities of the Kabardino-Balkarian Republic in this area were: the establishment of the Ministry of Foreign Relations of the Kabardino-Balkarian Repub-lic; the re-establishment of the Department of Foreign Economic Relations within it; the conclu-sion of interstate agreements with the Government of the Republic of Abkhazia, the Government of the Republic of Turkey and the Government of the Republic of Turkmenistan. Economic ties between the Kabardino-Balkarian Republic and the Commonwealth of Independent States began to develop. The number of joint Russian-foreign companies registered in the Kabardino-Balkarian Republic grew. Contracts were signed with large companies from Italy, Cyprus, Canada, France and China. Nevertheless, the success of joint ventures in the Kabardino-Balkarian Republic in the second half of the 1990s turned out to be insignificant, which was due to the strict federal tax and credit policies that hampered the development of production activities, the fall of the ruble ex-change rate and the military conflict in Chechnya, which caused a significant decrease in the in-vestment attractiveness of the Kabardino-Balkarian Republic and the entire North Caucasus at the international level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".