Perspective: Historical Disputes and Reconciliation in Northeast Asia: The Us Role
Bibliographic record
Abstract
Over the last two decades Northeast Asia has witnessed growing intra-regional interactions, especially in the realms of culture and economy. Yet wounds from past wrongs, committed during colonialism and war, are not fully healed and the question of history has become heated across Northeast Asia. In a 2006 survey, Chinese respondents listed the top four reasons for unfavourable views of Japan as related to history issues, led by the Nanjing massacre (42 percent). A similar survey of Koreans conducted in 2005 found that 93 percent felt unresolved historical issues are important to Korea-Japan relations/' East Asians have recognized the need for reconciliation and sought to achieve that goal through various tactics: apology politics, litigation, joint history writing and regional exchanges. While each approach has its own merits, none has succeeded. Despite Japan's efforts to apologize for its past, its neighbours continue to view the Japanese as insincere and remain skeptical of formulaic apologies. Almost all lawsuits that Asian victims have filed in Japanese courts have been either thrown out or left unresolved. Japan, China and Korea have yet to agree on a shared view of their past from joint history writing. And all nations, sharing a reluctance to fully confront the complexity of that past, tend to blame others.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".