Can the 'apology standoff' between China and Japan be resolved?
Bibliographic record
Abstract
In recent history there has been a move to making public apologies from a variety of countries and political leaders. Kevin Rudd apologised to the Australian Aborigines, Stephen Harper to the First Nations peoples of Canada and the US Congress and Senate to Native Americans and African-Americans for slavery. In the 2000s Germany apologised to the Herero and Nama people of Namibia for the genocide perpetrated on them at the start of the 20th century. However, the apology to the Jewish people from subsequent German governments after World War II (WWII) was probably the most challenging of all. The iconic (kniefall) picture of Chancellor Willie Brandt kneeling at the monument to victims of the Warsaw Ghetto Uprising summed up a genuine and heartfelt remorse that Germany had for its treatment of the East Europeans and the Jews in particular. In contrast, there is one nation, Japan, unable to construct an apology apparently sincere or wholehearted enough to satisfy, especially China, but also South Korea and other Asian countries.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".