Balancing Giants, Building Influence: Mongolia’s Soft Power in Asia
Bibliographic record
Abstract
This article analyzes Mongolia’s evolving strategic goals in Asia and the progress of its soft power development. Since the 1990s, Mongolia has pursued a balanced foreign policy through its “Third Neighbor Policy,” while advancing long-term modernization under Vision-2050 and the New Recovery Policy. These strategies have enabled Mongolia to diversify partnerships beyond China and Russia, strengthen cultural diplomacy, and expand educational and scientific exchanges. The country’s climb to 108th place in the 2025 Brand Finance Global Soft Power Index underscores recent achievements in media visibility, culture, education, and international relations. Cultural revival initiatives, international performances such as The Mongol Khan, branding campaigns like “#GoMongolia,” and academic cooperation have played central roles in raising Mongolia’s profile. Yet, significant constraints persist, including political instability, economic dependency on mineral exports, infrastructural limitations, and governance challenges. The study concludes that while Mongolia has enhanced its visibility and cultural appeal, its ability to transform soft power into sustained influence depends on governance reforms, institutional continuity, and deeper regional and multilateral engagement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".