Regional and International Cooperation to Reduce Nagorno – Karabakh Conflict
Bibliographic record
Abstract
Almost two decades since conflict broke out between the Republic of Azerbaijan and Armenia, two northern neighbors of Iran, in Nagorno – Karabakh region. Although military conflict in this region has minimized with the existing ceasefire, and reaching a sustainable agreement is likely to be happened by the two parties. Close regional and international cooperation seems necessary more than ever in order to reduce conflict in Nagorno – Karabakh region. The main objective of this study is to give a thorough presentation of the regional and international cooperation in order to reduce Nagorno – Karabakh regional issues and hazards.Hereunder is the main question raised by the researcher: “To what extent regional and international cooperation is effective in reducing Nagorno – Karabakh conflict?”The hypothesis of the present study is that the interrelated nature of security in the international system and Caucasus region causes convergence among neighboring countries to reduce conflict in Caucasus and Nagorno – Karabakh regions. Taking advantage of the analytic – descriptive method and also benefiting from reliable and authentic sources, it can be concluded that the interdependence of threat will lead to the increased mutual costs in this study. This issue will cause formation of convergence in the Caucasus region, so that it will lead to the reduced conflict and tension in Nagorno – Karabakh region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".