Mutual Perceptions and China-South Korea Relations: A Comparative Study of the Academic Literature
Bibliographic record
Abstract
How do China and South Korea see their relationship after 30 years of normalization, and why have views shifted since 2017? Research on perceptions and their foreign policy implications usually draws from offcial discourse and public opinion. This review essay assesses the nature and drivers of China-South Korea mutual perceptions by comparing their academic literature on bilateral relations. Scholarly accounts may offer longer-term interpretations of specialized interests, and a fuller picture of how and why views vary. On both sides of the China-South Korea academic debate, the quantitative volume of studies and qualitative appraisal of relations declined in the 2017???2021 Xi Jinping-Moon Jae-in period. Levels of optimism/pessimism vary by issue-area. Views of third-party constraints on security relations, and domestic political influences on societal relations, drive mutual pessimism. Koreans are more pessimistic about the economic partnership and reassess historical relations more unfavourably, which trace back to views of relative dependence and hierarchy. Three implications emerge for post-2022 relations in light of leadership transition in Beijing and Seoul. Enduring security priorities require minimum strategic interdependence and stronger trust-building mechanisms. Positive functional spillovers from economic and local/nonstate cooperation remain in question. And lasting cultural costs of political disputes compel joint efforts to enhance mutual understanding. Overall, shifts in structural and ideational factors that historically drove normalization are driving the current discord, and prompting both sides to lower future expectations of each other.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".