An Appraisal on China-Canada Military Relation in the War of Resistance Against Japan
Bibliographic record
Abstract
When China engaged in the War of Resistance against Japan from 1937,Canada adopted the policy of appeasement,but took some measures for the war-preparation. China-Canada relations were on a low level till the beginning of the War. However,China-Canada military cooperation was started after Canada entered the war in Europe. Although Canada's view on Kuomintang's war efforts was somewhat negative,it adhered to the cooperation and provided China with the assistance in information and war-material goods. Therefore,the close military relation corresponded that in politics between the two countries; the establishment and development of the military cooperation redounded to the Anti-fascist War in the world; the main channel of this cooperation was though Canada's war-material goods aid to China; General Odlum played a prominent role in it; the overall impact of the cooperation was relatively limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".