How has the media's construction of a discourse of nationalism evolved? Critical discourse analysis of Korean sports nationalism through the FIFA World Cup
Bibliographic record
Abstract
Drawing on Fairclough's critical discourse analysis, this study sheds light on sports nationalism and traces changes in the media's discursive construction of the Korean national football team over time by analyzing news articles from three different World Cups. Korean sports nationalism has evolved in complex ways and has been influenced by a combination of factors, including the colonial experience, initiatives by the former military government, the hosting of mega-sporting events, local professional leagues, and the globalization of athletes on the world stage. Given the multifaceted nature of Korean sports nationalism, this study aims to examine how it has changed in response to social transformations, particularly the impact of neoliberal globalization. The findings reveal that sports nationalism is often manifested in the terms of “fighting spirit” or “sacrifice” as a core national trait. However, it increasingly incorporates and embraces a new neoliberal meritocratic culture and subjectivity that foregrounds individual success on the world stage as a new form of nationalism in an era of accelerating globalized and commodified sports elitism.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".