Orphan Works: A Comparative Analysis of the United Kingdom, Canada, and Australia Regarding Copyrights and its Implications for the United States of America
Bibliographic record
Abstract
Arguably one of the most prevalent issues in the field of Intellectual Property law, both international and domestic, is that of the emerging orphan works problem. Orphan works are any original literary, pictorial or graphic illustrations, and photographs whereas the prospective user cannot readily identify and/or locate the owner(s) of the copyrighted material. This poses a legal risk of liability upon the prospective user for copyright infringement. This thesis focuses on the legal topic of copyright with an emphasis on orphan works legislation. This study compared and contrasted the experiences in the United Kingdom, Canada, and Australia which have all enacted legislation to mitigate the issue of liability to prospective users of orphaned works, to the United States which has been reluctant to do the same. Each country has used its own legislative model to mitigate the liability of orphan works. This study sought out to analyze each model as well as compare the legal, political, and economic similarities of each country to test the viability of a particular model being successful in the United States.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".