What are some significant factors that affect the prejudice between east Asian countries, China, Japan, and Korea specifically?
Bibliographic record
Abstract
The purpose of this paper is to discuss factors that affect prejudice between China, Japan, and Korea will be discussed. The primarily negative relationship between these East Asian countries has been impacting the lives of citizens throughout history, especially now, with the help of social media. Harmful stereotypes such as “cold and unreliable Japanese”, “untrustworthy Chinese”, and “culture stealing Koreans” cause discourse such as the Chinese people rejecting aid that the Japanese government sent during COVID-19, the school textbooks that paint each other in a negative light by using “fake history”, and fights that often break out between Chinese, Japanese, and Korean people on social media. Through researching articles previously written on this subject, I found that some factors that might affect the prejudice between these three East Asia countries are views of historical animosity, such as atrocities committed by Japan in World War II and Imperial Japan’s invasions, political needs within the countries, and current military advances including the fight of claim over the Diaoyu islands in the East China Sea. Ultimately, these stereotypes and prejudice are not backed by any scientific reasoning, being mostly motivated by amplified emotions caused by propaganda. This paper seeks to shed light on the prejudices that affect the relationship between Chinese, Japanese, and Korean people and help them understand each other better, which is the first step towards dispelling unreasonable stereotypes. Sources from various East Asian and Western perspectives will be examined in tandem to alleviate the possible bias present in the analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".