Bibliographic record
Abstract
The japanese assault on the city of nanking in December 1937 was one of many incidents that the International Military Tribunal for the Far East (IMTFE, 1946–48) examined in the course of judging the wartime leaders of Japan. What was referred to at the time as the “Rape of Nanking” has in the last several decades become a controversial marker of Chinese identity as well as a source of potent disagreement among Japanese over their nation's history as a colonial power in East Asia. Within this controversy, the IMTFE trial in Tokyo has been used as a touchstone to confirm and deny all manner of claims concerning the incident. Those who feel aggrieved over Japan's conduct toward China cite the evidence produced at the trial to authenticate the scale and brutality of the massacre (Eykholt 2000, 19–23). Those who feel that Japan and the emperor system have been unfairly blamed for the war in East Asia scour the trial proceedings for failures of logic and evidence that demonstrate to their satisfaction that the “Tokyo trial view of history” is nothing but anti-Japanese distortion and fabrication (Yoshida 2000, 111–14). For both sides, the Tokyo judgment is fuel for ideological fire.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".