Decoding Orphan Works Policies: Lessons from the European Union, the United Kingdom, Canada, and India
Bibliographic record
Abstract
Orphan works are works (e.g., books, photographs, films) that are still protected by copyright law but whose copyright owners are untraceable by prospective users. The exercise of searching for the copyright owners before exploiting their works is critical in copyright law, as failure to do so would constitute copyright infringement. This aspect, however, cannot be met because the copyright holders are either unknown or untraceable. Globally, the discussions in this area are primarily focused on developing legal mechanisms to legalise the use of orphan works. For example, the suggestion to use the copyright statute's fair dealing defence and the proposal to implement a specific legal exception for the use of orphan works. The trend to examine orphan works policies, on the other hand, is not heavily discussed by the copyright society, despite the fact that this aspect is critical in understanding certain basic principles of the relevant laws. In this light, the purpose of this study was to fill the gap by examining the relevant orphan works policies in the selected jurisdictions by using policy analysis. The purpose is two-pronged. Firstly, to identify the similarities and unique characteristics of the orphan works policies. Secondly, to extract the basic principles and other important information that policymakers can use when developing their own version of orphan works policies and laws. Among the key findings are the importance of maintaining the goal of knowledge dissemination from orphan works and implementing the principle of openness to promote free movement of knowledge and innovation. It is hoped that this research will aid policymakers and legislators in better understanding the issue and developing a more robust solution.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".