The diplomatic roles of Korean state-run sport for development programs
Bibliographic record
Abstract
The Korean government established the Korea International Cooperation Agency (KOICA) in April 1991 as an agency of the Ministry of Foreign Affairs (MOFA) to design and execute most of its official development assistance (ODA) grants. Since then, KOICA has administered two forms of Korean sport grants: sport aid projects (e.g. the construction of sport facilities and provision of sport equipment) and sport technical cooperation programs (sport volunteering and Taekwondo coaching programs). Drawing on Murray’s (2018) categorization of sport diplomacy, as well as Foucauldian discourse analysis, we examine how KOICA sport initiatives have, over three decades, operated to support the government's foreign policy and diplomatic goals. The findings reveal that KOICA sport initially prioritized elite sport development in an approach akin to traditional sport diplomacy. Now, however, it appears to have adopted global sport for development (SFD) strategies with a focus on social development, in line with a new sport-oriented, public diplomacy approach. Through the combination of these two strategies, the role played by KOICA sport as a diplomatic tool of the Korean state has become more sophisticated.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".