Anti-Chinese and Anti-Japanese Sentiments in East Asia: The Politics of Opinion, Distrust, and Prejudice
Bibliographic record
Abstract
Spanning the presidencies of George W. Bush and Barack Obama, anti-Americanism has become somewhat of a growth industry. Its products are avidly consumed not only in the United States but around the world, often for quite different political purposes. It is not surprising that anti-Americanism plays a large role among traditional enemies of the United States in the Middle East.1 But the same is true of America's allies just across the border, such as Canada and Mexico, and living far apart, such as Australia and New Zealand.2 It would be a big mistake, however, to think that these oppositional sentiments—the complex mixture of opinion, distrust and prejudice—are directed only against America. It plays a role in all parts of the world. Secondary or subordinate states in each world region resent the regional top dog. Such resentment can take various political forms. In Europe, latent anti-German feelings linger, sometimes very close to the surface. When Germany takes a stance against its partners in the European Union, as it has at various times in the unfolding financial crisis, historical anti-German sentiments are quick to appear. Small states in Latin America and Africa also harbour resentment against the self-proclaimed leadership roles of Brazil and Nigeria. And in the Middle East, cross currents of nationalism and religion generate different kinds of political resentment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".