Bibliographic record
Abstract
This article presents a small state’s perspectives on military socialization by examining military interactions at political and strategic, operational, and tactical as well as educational tiers. Transnational military socialization creates learning and socializing environments for militaries. However, the transnational military socialization process in Asia has received little scholarly attention, although its nature changed dramatically after the Cold War and 9/11. A small state often enters into asymmetric socialization with major powers. Mongolia presents an interesting case to capture these changes and impacts of transnational military interactions. The Mongolian military internalized Soviet military norms, ideas, and values during its seventy-year military socialization process. Since then, it has been attempting to disconnect from its communist past by internalizing the Western military norms, ideas, and culture even though it is isolated from the Western world. Interactions between Sino-Mongolian militaries over the past two decades have not resulted in any in-depth exchanges of ideas due to political, historical, and cultural factors. In the absence of explicit pressures from these three major powers, today the Mongolia military has consolidated a new identity, similar to a Western military. It has increased its contribution to peace support operations while attempting to disconnect its Soviet past and avoiding absorption from the growing Chinese military initiatives toward Central Asia. At the same time, Mongolia’s military socialization with Russian or American militaries often triggers uncertainty for Chinese security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".