Problems and Reform of Korea’s Copyright Royalty Determination System: Proposals for Structural Improvement
Bibliographic record
Abstract
Korea’s current system for determining copyright royalties has played a role in preventing monopolistic practices by copyright trust management organizations and in maintaining fair-trade practices. The system has functioned as a form of government intervention aimed at balancing the conflicting interests of copyright holders and users, while also supporting the broader goal of advancing the cultural industries. However, in today’s rapidly evolving environment—marked by digital innovation, diversification of media content, and the rise of platform-based industries—the existing state-approval mechanism is criticized for its inflexibility, its constraints on service innovation, and its potential to undermine the core values of copyright, namely autonomy and creativity. In particular, the principle that prices in the private sector should primarily be determined through free market negotiations is gaining support. The current state-driven approval process for copyright royalties imposes excessive administrative burdens, delays, and uncertainty. Paradoxically, by restricting the autonomy of contractual relationships between rights holders and users, the system may generate more disputes and unnecessary social costs. This study diagnoses the structural problems inherent in the current royalty determination system and, from a practitioner’s perspective, presents practical directions for improvement by conducting a comparative legal analysis of copyright royalty determination frameworks in major jurisdictions, including Japan, the United Kingdom, Germany, the United States, and Canada.
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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 | 0.032 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".