The Future of Crises in South Caucasus in the Wake of Russia-West Conflicts
Bibliographic record
Abstract
South Caucasus region due to various reasons including ethnic diversity, religions and geographical position has long been witnessing various crises such as Karabakh and south Ossetia crisis. Among these, Karabakh crisis has a direct impact on the national interests of Islamic republic of Iran; because, this crisis has been developed in the northern borders of Iran and between the two countries of Azerbaijan and Armenia that in addition to neighboring Iran, both share some historical, cultural, ethnic, and even religious commonalities with Iranian people. In this study, the main question is that in case of failure of the West in confrontation with Russia in Ukraine, Syria, and Iraq, will the West, to compensate its failure, use these crises of the South Caucasus region to inflict the security and interests of Russia? Almost certainly, if the victories of Russian-oriented groups in different areas of the Ukraine crisis as well as the occurrence of significant victories for the government and the people of Syria and Iraq in fighting terrorism such as ISIS and Al-Nusra groups particularly success in reclaiming the occupied cities from terrorists, the West will surely take actions against interests and security of Russia. The triggering one of the dormant crises of south Caucasus by the west is more likely than other crises exist within the borders of the Russian federation and central Asia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".