Korean sport for international development initiatives: exploring new research avenues
Bibliographic record
Abstract
The use of sport to promote social development and economic growth has been studied by Korean scholars focusing on the domestic effects of mega sport events or of ‘sport for all’ initiatives. Meanwhile, the concept of ‘sport for development’ (SFD) in English-language sport studies literature nowadays tends to focus on the use of sport as a strategy to foster international development, wherein sport initiatives are funded and facilitated by industrialized nations to support the socioeconomic growth of developing economies. However, Korean sport scholars examining initiatives similar to SFD practices focus on the benefits of SFD for South Korea with regard to sport diplomacy and have yet to further enhance our understanding of these programmes’ role in advancing international development and relations through theoretically informed critical analysis. In this article, we review Korean research on the contribution of sport to domestic social and economic development as well as sport diplomacy research on Korean international SFD programmes and we summarize Western research on sport for development by focusing on the use of the social capital and the critical theory approaches. We conclude by suggesting how these theoretical frameworks used in Western research could be introduced in order to enhance current research on Korean international sport initiatives as we invite Korean scholars to diversify and expand the current thinking on sport for international development.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".