Copyright in the Art Industry: Ethical and Management Challenges for Artwork Protection
Bibliographic record
Abstract
Indonesia, with its diversity of arts and culture, faces unique challenges in copyright protection in the digital era. Globalization and digital technology have transformed the landscape of the arts industry, introducing both new opportunities and complex risks. The aim of this research is to explore the ethical and managerial issues related to copyright protection of artistic works in Indonesia. This study employs a qualitative approach with a focus on literature review. Copyright protection in the Indonesian arts industry encounters several challenges, including ineffective law enforcement, the gap between technological advancements and regulations, and the need for more adequate legal infrastructure. The importance of enhancing legal awareness and public education, as well as the necessity of collaboration among government, relevant institutions, and the industry in addressing these challenges, cannot be underestimated. Furthermore, adaptive regulatory updates and responsiveness to technological advancements, the enhancement of ethical awareness in the use of artistic works, and the implementation of cutting-edge technologies such as artificial intelligence and blockchain are strategic steps in strengthening copyright management.
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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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".