Liberating Orphan Works from The Copyright Orphanage: The Malaysian Perspective
Bibliographic record
Abstract
Orphan works are works that are still protected by copyright and whose owners cannot be identified or located by prospective users for copyright clearance. Many countries have addressed this issue since the emergence of the problem, and it remains a legitimate subject of inquiry in this present day. However, Malaysia is yet to initiate public consultations and formulate legislative and non-legislative solutions to the orphan work problem. Hence, this paper aspires to underline the challenges and obstacles in exploiting the orphan works in Malaysia. It starts with a brief introduction to the orphan works problem and its causes. It further highlights the legal and policy uncertainties about the orphan work phenomenon in Malaysia and its implication to higher learning education. Besides, this paper also examines the current practices in the United Kingdom and Canada. Finally, this paper proposes some suggestions into what Parliament and policymakers have to do and avoid when solving Malaysia's orphan work phenomenon. It is hoped that the access to the orphan works in Malaysia would not be problematised, thereby liberating them from the copyright orphanage. Keywords: Copyright law, Orphan works, Orphan works licensing scheme
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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".