Tactical Changes of the Chosŏn Military in the First Year of the East Asian War
Bibliographic record
Abstract
For the first three months of the East Asian War, Chosŏn commanders learned how fleetly the Japanese armies, equipped with keen swords for close combat and dreadful muskets for long-range shooting, marched due to their adroit maneuvers. This article examines the way the Chosŏn armies made tactical adjustments during the East Asian War, especially from the third quarter of 1592 to the first quarter of 1593, while at the same time avoiding a direct confrontation with the Japanese armies. One focus of this paper is upon how the Chosŏn armies opted for defensive fortifications, depended on infantry-centered operations, and achieved some meaningful victories. The other focus is upon how the tactical changes had a bearing on Sino-Korean military collaboration and the resumption of Sino-Japanese negotiations. This two-tiered approach will place Chosŏn perspectives in line with recent research on the interstate scale of the war, where infantry warfare and firearms became one major strategy of Chosŏn and Ming against “the northern caitiffs (the Mongols/Jurchens) and the southern dwarves (Japan) 北虜南倭” in the sixteenth century and beyond, and illuminate the complex interstate relations among the East Asian countries that couched Ming-centered regional hegemony in terms of their own security.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".