Bibliographic record
Abstract
Introduction, Kimitaka Matsuzato Explanatory Notes Map Chapter 1: Russia's Expansion to the Far East and Its Impact on Early Meiji Japan's Korea Policy, Shinichi Fumoto Chapter 2: Russian Factor Facilitating the Administrative Reform in Qing Manchuria in the Late Nineteenth and Early Twentieth Centuries, Susumu Tsukase Chapter 3: Imperial Ambitions: Russians, Britons and the Politics of Nationality in the Chinese Customs Service, 1890-1937, Catherine Ladds Chapter 4: Development of Trade on the Amur and the Sungari and the Customs Problem in the Last Years of the Russian Empire, Yukimura Sakon Chapter 5: Making a Vancouver in the Far East: The Trinity Transportation of the Chinese Eastern Railway, 1896-1917, Masafumi Asada Chapter 6: Japanese-Russian Kulturkampf in the Far East, 1904-5: Organization, Methods, Ideas, Dmitrii B. Pavlov Chapter 7: Captured or Captivated? War against Japan (1904-5) in the Memories of Russian POWs, Andreas Renner Chapter 8: From the Meiji Emperor's Funeral to the Taisho Emperor's Coronation: Reporting the Japanese Imperial System in the Russian Press, Yoshiro Ikeda Chapter 9: Two Russias in Harbin: Emigre Community and the Soviet Colony, Michiko Ikuta Chapter 10: V. L. Kopp and Soviet Policy towards Japan after the Basic Convention of 1925: Moscow and Tokyo's Failed Honeymoon?, Yaroslav Shulatov
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".