Orphan Works in Copyright Law: Causes, Challenges, and Comparative Legal Responses
Bibliographic record
Abstract
This article examines the legal, structural, and technological factors that contribute to the growing problem of orphan works within contemporary copyright systems. It explains how the removal of registration formalities, the steady extension of copyright duration, and weak metadata practices have made ownership identification increasingly difficult, particularly in the digital environment. Using a doctrinal and descriptive research methodology, the paper draws on domestic legislation, international treaties, judicial decisions, and policy reports to assess the scale of the problem and its practical consequences for authors, users, libraries, and cultural institutions. Comparative analysis of approaches in the United States, the European Union, Canada, and India highlight divergent regulatory models ranging from fair use doctrines to licensing and centralized orphan works registries. The discussion shows how legal uncertainty discourages preservation, digitization, and lawful reuse, resulting in a broader loss to public access and cultural memory. The article concludes by outlining policy options and preventive measures, emphasizing the role of diligent search standards, limited liability frameworks, and improved rights information systems in balancing copyright protection with public interest objectives.
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 | 0.036 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.014 | 0.057 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".