Enhancing Access and Facilitating Use of Orphan Works: A Policy-Led Framework
Bibliographic record
Abstract
Background and Purpose: Orphan works, which are copyright-protected works with unidentified right holders, are increasingly common as a result of the abundance of unregistered, unidentified, and abandoned copyright works. In contrast to the European Union, United Kingdom, Canada, and India, Malaysia is yet to devise a solution to the orphan works problem, much less to enact an orphan work legislation or policy. Such a scenario could expose potential users to the risk of being sued by the reappearing copyright holders. Such issues can thus impede the potential use and re-use of orphan works for the purpose of knowledge dissemination as well as progress in the arts, preservation, and digitisation activities. Methodology: In overcoming the legal impediments relating to the use of orphan works, this research proposed a solution in the form of a policy. Such a policy should be developed by incorporating standard practices from other countries. In doing so, this research conducted an analysis of orphan work policies (policy analysis) from the selected jurisdictions. Findings: The findings (i.e aims of exploitations, principles of exploitations) from such exercise are proposed to be incorporated when formulating a policy of orphan works in Malaysia. Contributions: The proposal serves as a novel contribution of this paper to the body of knowledge. In the absence of a legislative solution, it is hoped that the proposed policy on orphan works will assist stakeholders in better managing orphan works held in their repositories and lay the groundwork for the legislation of orphan works law in Malaysia.
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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 | 0.056 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.027 | 0.027 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".